Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture

Deepashri KM; J Satheesh Kumar; Santhosh KV

doi:10.12688/f1000research.159518.2

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Research Article

Revised

Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture

[version 2; peer review: 1 approved, 2 approved with reservations]

Deepashri KM¹, J Satheesh Kumar¹, Santhosh KV ²

PUBLISHED 07 Jul 2025

Author details Author details

¹ Dayananda Sagar College of Engineering, Department of Electronics and Instrumentation, Visvesvaraya Technological University, Belagavi, Karnataka, India
² Manipal Institute of Technology, Department of Instrumentation and Control Engineering, Manipal Academy of Higher Education, Manipal, Karnataka, India

Deepashri KM
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation

J Satheesh Kumar
Roles: Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Santhosh KV
Roles: Conceptualization, Investigation, Methodology, Software, Supervision, Validation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Agriculture, Food and Nutrition gateway.

This article is included in the Manipal Academy of Higher Education gateway.

Abstract

Background

Current advancements in communication and information have important impacts on the agricultural sector. Technology has been instrumental in developing innovative approaches to enhancing farming productivity and efficiency while also addressing environmental concerns. With the aid of technology, researchers can collect and analyze vast amounts of agricultural data, enabling a deeper understanding of farming practices and facilitating more informed decision-making through cutting-edge techniques.

Methods

The study on Capsicum phenology introduces a nuanced approach by integrating statistical analysis specifically t-test and ANOVA to examine environmental parameters across various growth stages. This methodology offers a more detailed understanding a factors like temperature, humidity and soil moisture influence Capsicum development, providing a statistical foundation for adaptive crop management strategies.

Results

The results demonstrated substantial variability in these parameters, emphasizing the importance of tailored crop management strategies.

Conclusion

This research bridges a gap in Capsicum specific phenological studies and also sets a precedent for integrating statistical analysis with a smart agricultural technology paving the way for the development of autonomous crop management system that adapt to specific crop needs, thereby enhancing productivity and sustainability in agriculture.

Keywords

Crop parameters, Crop phenology, Statistical analysis, t test, ANOVA, Crop management system

Corresponding author: Santhosh KV

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 KM D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: KM D, Satheesh Kumar J and KV S. Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture [version 2; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:1516 (https://doi.org/10.12688/f1000research.159518.2) First published: 12 Dec 2024, 13:1516 (https://doi.org/10.12688/f1000research.159518.1) Latest published: 07 Jul 2025, 13:1516 (https://doi.org/10.12688/f1000research.159518.2)

Revised Amendments from Version 1

As per the suggestions received by the reviewer, the justification for the methods and conclusion are made in the Abstract, Methods The study uses statistical analysis, specifically t-test and ANOVA, to examine environmental parameters influencing Capsicum development, providing a foundation for adaptive crop management strategies.
Conclusion This research enhances Capsicum-specific phenological studies and integrates statistical analysis with smart agricultural technology, paving the way for autonomous crop management systems for increased productivity and sustainability.
In the Introduction, highlighted the advantage of integration statistical analysis: The Capsicum study is a significant advancement in crop management, integrating statistical tools with agricultural data and autonomous management systems to understand phenology and environmental interactions.

Impact of climate change on agriculture is discussed in Literature survey: Climate change adaptation is crucial for agricultural sustainability, requiring increased production, sustainable land management, local knowledge, and policy implementation. Research on climate change's impact on specific crops varies across ecological zones and depends on available resources.
[Cited two articles for the climate change adaptation and included the articles in the references]

The adoption of min-max normalization technique in the Method: The min-max normalization technique scales environmental parameters across four Capsicum phenological stages, making it suitable for fuzzy logic algorithms requiring bounded input.

Summary: The study uses statistical analysis, specifically t-test and ANOVA, to examine environmental parameters influencing Capsicum development, providing a foundation for adaptive crop management strategies. It enhances Capsicum-specific phenological studies and integrates statistical analysis with smart agricultural technology, paving the way for autonomous crop management systems for increased productivity and sustainability. Climate change adaptation is crucial for agricultural sustainability, and the min-max normalization technique scales environmental parameters across four Capsicum phenological stages.

See the authors' detailed response to the review by Dua Weraikat

Introduction

To fulfil the rising need for food, water, and energy, increasing socioeconomic development and population increase, resulting in new problems related to agriculture. To effectively safeguard the sustainability of resources and manage the impact of future agricultural output, rethinking is needed. By employing a combined energy-water-food connection optimization technique, it has been found that it is currently not possible to manage agricultural resources and waste in accordance with agricultural management principles to achieve sustainability.¹

The advancement of agricultural modernization is strongly supported by agricultural information. The implementation of modern technological advances to provide relevant and helpful data to users through agricultural information suggestion services has been shown to be a successful remedy with the ongoing evolution of agricultural information infrastructure construction.² This promoted the creation of autonomous farming systems with a primary focus on irrigation, climate control, and greenhouse monitoring for field work, animal management, and growth control. Effective agricultural production management, which increases both the productivity and safety of agricultural products, can reduce negative environmental effects. Technologies based on sensors are essential in the expanding field of agriculture. Farmers, scientists, and technological manufacturers are working together to develop practical solutions that will increase productivity and result in cost savings.³

Agriculture depends on numerous economic and climatic conditions. The variables that affect agricultural crop production include soil, weather, cultivation, temperature, irrigation, fertilizers, rainfall, harvesting, pesticide and insecticide usage, weeds, and other variables. The study of vital phenomena in plants is known as plant physiology. Understanding different plant biological processes, such as photosynthesis, respiration, transpiration, translocation, nutrient uptake, hormone-regulated plant growth, and other activities that have a significant impact on crop yield, is helpful.⁴

The profound integration of both contemporary information technology and conventional farming has given rise to the “smart agriculture” era. Smart agriculture addresses automation and intelligence in agriculture.⁵ Nonetheless, the concerns of statistical security cannot be ignored considering the growth in agriculture facilitated by modern digital technologies. In agriculture, statistical analysis has an important impact on the collection, analysis, and interpretation of numerical data. Agriculturalists can utilize data analytics to develop predictive analytics for future yields, manage resources based on existing trends, and continuously check the health of their crops,⁶ maximizing profitability and minimizing waste. In contrast there are existing models for crop management also employed statistical analysis to understand phenology and environmental interactions. Crops may require a specific range of temperature, humidity and soil moisture for optimal growth. However, the Capsicum study’s emphasis on integrating statistical tools with agricultural data and autonomous management systems represents a notable advancement in the field. Understanding the optimal conditions for crop growth, farmers can apply these resources in a targeted manner, reducing waste and maximizing their impact.

Literature survey

The utilization of IoT devices for monitoring and controlling crop production is prevalent,⁷ but rural areas often face challenges due to unstable cloud connectivity. Fog computing technology addresses this issue by managing sporadic connectivity and enabling faster data analysis. The experiment employed two datasets: one with air humidity and temperature values and another with soil temperature and moisture values. Fog filters apply unsupervised machine learning techniques to cluster unlabeled data and use supervised learning classification techniques to predict data sample categories.

A comprehensive analysis of 61 selected studies from 221 publications focused on advanced time series models in agriculture.⁸ Historical data and information techniques for applying time series models are discussed. This review highlighted that deep neural networks enhance simulation and data structure mining capabilities, contributing to end-to-end optimization for environmental prediction. This approach broadens the range of environmental characteristics that agricultural facilities can manage.

A systematic review of agricultural IoT presented the current state, system architecture, and five main IoT technologies.⁹ Five chosen fields for IoT applications in agriculture were introduced, along with an analysis of the challenges and future growth predictions. The key issues identified include network and interoperability problems, trust, environmental sustainability, and insecurity. An evaluation of IoT technology in agri-food supply chains identified 24 crucial criteria categorized into technological, social, economic, and organizational aspects.¹⁰ The DEMATEL approach was used to determine causal relationships, highlighting trust, environmental sustainability, insecurity, interoperability, and network issues as significant factors influencing IoT adoption. These insights can help overcome obstacles in the agri-food sector.

Research on tomato farming in Mayotte aimed to understand production practices and technical choices to promote environmentally friendly practices.¹¹ Field inspections and farmer interviews revealed the overapplication of pesticides and a lack of correlation between application rates and crop health. This study indicated a need for better information and practices related to agricultural health protection.

Data mining techniques such as clustering and regression were used to analyze agricultural data, identifying optimal parameters for enhancing crop production.¹² This approach helps improve productivity and climate change resistance by analyzing new and existing data on crops, soil, and climate. An agricultural management system was designed to increase productivity and profitability through effective practices and online product sales.¹³ Data fusion techniques combine information from various sources for accurate remote findings, aiding intelligent decision-making for resource management based on seasonal and environmental conditions.

The state of modern farm management systems was assessed, highlighting each stage from data collection to decision-making.¹⁴ Using AI and robotic technologies, data-driven managed farms can enhance productivity and reduce environmental harm, establishing a foundation for sustainable agriculture.

Agricultural development data from government publications were analyzed using correlation and multiple regression to understand the relationships between cropping intensity and various production performance factors.¹⁵ The study revealed significant variance in crop yields influenced by factors such as irrigation and climate conditions. Big Data Analytics was used to construct a weather-based crop prediction system in India.^16,17 Data on various climatic and soil factors were preprocessed and analyzed using the MapReduce framework and k-means clustering. The system provided accurate predictions and was presented through a graphic user interface, aiding agriculturalists in optimizing yields. Systems thinking was implemented to improve agricultural land production by using an internet-based causal loop diagram.¹⁸ This approach helps measure variables such as temperature, humidity, and pest activity, addressing issues in planting and pest control.

A comprehensive examination of data mining software in agriculture classified methods for crop production monitoring.¹⁹ This study suggested an intelligent crop management system to help farmers make informed decisions based on various environmental and crop parameters. Understanding soil water and salinity variations is crucial for effective irrigation and fertilization management.² Studies have provided 48-hour estimates of soil salt and water levels, aiding prediction and management in semiarid regions.²⁰ Effective planning and yield assessment are essential in agriculture. The use of data mining techniques for making critical farming decisions, considering commodity prices, soil variance, and environmental factors, was emphasized. Machine learning techniques such as random forest regressor and linear regression were used to analyze agricultural data and optimize crop productivity.^21,22 To address the challenges of climate change and food production, climate-smart farming methods were compared with traditional methods. Irrigation and smart crop management techniques significantly increase yields, demonstrating the potential of data fusion models in optimizing agricultural practices.^23,24

Climate change adaptation is crucial for agricultural sustainability, requiring increased production, sustainable land management practices, local knowledge, policy implementation. Technical measures and local knowledge contribute to adapting agriculture to climatic variability, but research on the impact of climate change on specific crops varies across ecological zones and depends on available resources.²⁵

As Capsicum is temperature sensitive, 25 to 30°C is the ideal growing temperature. Temperature variations can have an impact on metabolite accumulation rate, capsaicin synthesis, fruit germination, harness, maturity, as well as physiological growth.²⁶

This literature survey highlights the diverse applications of smart agriculture technologies, focusing on IoT, fog computing, data mining, machine learning, and climate-smart farming methods to enhance productivity, sustainability, and resource management in agriculture.

Despite advancements in statistical analysis tools for deciphering crop parameter levels, there remains a significant gap in understanding the inherent variability in crop phenology. Different crops exhibit unique growth stages and respond variably to environmental conditions, posing a challenge in developing generalized models. Research needs to focus on creating more crop-specific models that can accurately capture these unique phenological responses and environmental interactions.

The primary focus of analyzing agricultural parameters is twofold: to assist researchers in expanding their knowledge base and to ensure that farmers benefit from improved crop outcomes and effective field management. Through careful examination of the data, researchers can identify patterns, correlations, and trends, enabling them to make targeted recommendations for optimizing crop production. The vision of designing an autonomous crop management system that is both technologically advanced and flexible remains largely unfulfilled. Existing systems often lack the necessary adaptability to cater to the diverse requirements of different crops and their unique phenologies. Research should focus on developing more flexible, adaptive systems that can tailor interventions and recommendations specific to each crop and its environmental conditions. In conclusion, ongoing advancements in agricultural research, coupled with the application of sophisticated analysis tools, are instrumental in ushering in a new era of precision farming. The goal is to design autonomous systems that not only streamline farming operations but also adapt intelligently to the nuanced needs of different crops, thereby fostering a more sustainable and productive agricultural landscape.

Method

An agricultural crop management system refers to the combination of various technologies, such as sensors, robotics, and artificial intelligence, to automate and optimize the tasks involved in crop cultivation. Statistical analysis plays a crucial role in agricultural crop management systems. In this work, the capsicum crop growth²⁷ experimental dataset was used for the analysis. The dataset consists of agricultural parameters such as temperature, humidity, and soil moisture (at three different levels, SM1, SM2, and SM3) and pressure and light intensity recorded for three months. The Capsicum crop dataset available was recorded during the months of March 2020 (328 samples), April 2020 (2880 samples), May 2020 (2976 samples) and June 2020 (1004 samples) using Libelium hardware. In this work, the temperature, humidity, and soil moisture (SM1) datasets are considered for statistical analysis.

The dataset was analyzed and compared with the available phonological (growth) stage data of Capsicum crops.^28,29 Surveying and consulting agriculturalists revealed that the phenological stages of Capsicum consisted of four growth stages, as shown in Table 1. The data were further divided into four phenological stages.

Table 1. Phenological stages of the Capsicum crop.

Growth stage	Stage duration (days)
Vegetative	25
Flowering	10
Fruit set	10
1^st Harvest	25

The capsicum seeds are initially seeded in trays or cavities. Seeds germinate approximately one week after sowing. The seedlings in trays are transplanted to fields or planting beds within 30-35 days.¹⁴ After transplanting the plants to the vegetative stage followed by flowering, the fruit set and harvest stages of the Capsicum plants were evaluated. The experimental dataset was large, and temperature, humidity, and soil moisture data were available for different growth stages of Capsicum. Box charts are useful visual tools for summarizing and analyzing large datasets. Figure 1 shows that the box plot of temperature data for crop growth stages provides insights into central tendencies, variability, and outliers, which helps to guide decisions related to crop management and identify potential areas for improvement.

Figure 1. Box plot of the temperature data.

A box plot of the humidity data is shown in Figure 2, which provides a visual representation of the humidity variability and helps to identify typical, extreme, and unusual conditions. A box plot of the soil moisture data is shown in Figure 3. It helps to assess and understand the soil moisture patterns and identify areas that might require attention or action. The methodology for conducting the statistical analysis of the agricultural parameters of temperature, humidity and soil moisture is shown in Figure 4. Statistical analysis is crucial for understanding and optimizing crop growth and farm management. In this work, the temperature, humidity, and soil moisture dataset of Capsicum was used for statistical analysis. Through surveying and consulting agriculturalists, the Capsicum dataset was divided into four phenological stages. Understanding plant phenology is important for designing a crop management system for adapting to changes in climate and ecological interactions. The min-max normalization technique is used to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for fuzzy logic algorithms requiring bounded input.

Figure 2. Box plot of the humidity data.

Figure 3. Box plot of the soil moisture data.

Figure 4. Methodology for the statistical analysis of agricultural parameters.

Statistical hypothesis tests were performed to determine whether there were significant differences or effects related to the parameters. A significance level of α = 0.05 was used to compare the computed p values. A p value of less than 0.05 indicates statistical importance and rejection of the null hypothesis. Statistical analysis is vital in autonomous crop management systems because it enables data-driven decision making and optimization of agricultural resources. Agriculture is increasingly utilizing data for efficient decision-making, enhancing productivity and sustainability. Smart farming is being driven by advancements in data management, utilizing objective information acquired through sensors to minimize resource misuse and environmental pollution. Data-driven agriculture, which involves the incorporation of robotic solutions and artificial intelligence techniques, aims to transform food production to meet population growth needs while saving money.

Statistical analysis

A new crop variety or production method may significantly boost food production in a particular area. Adaptive research aims to assess a specific innovation’s effectiveness considering regional circumstances.³⁰ With the help of modern agriculture, new methods for increasing farming productivity and efficiency while protecting the environment have been developed. To better comprehend farming tasks and help farmers, agronomists, and experts make decisions, the gathering and examination of enormous agricultural datasets were made possible by current, highly advanced digital equipment and data science.

The use of agricultural technologies, including improved varieties and chemical inputs, is influenced by various variables, such as involvement in organizations, tenure of land, utilization of credit, farmer education, family size, and field size. However, adoption rates differ greatly in terms of geography, technology, and social context. Education in improved varieties may be replaced by extension services, while land tenure encourages natural resource management techniques. Therefore, initiatives to advance agricultural technology in emerging countries must be tailored to local contexts.²⁹ The goal of an autonomous crop management system is to improve its adaptability and usefulness under local conditions. The statistical analysis of agricultural crop parameters is essential for quality control, experimentation, and risk assessment in agriculture. It enables farmers to make informed decisions about resource allocation, risk management, and farming practices to maximize crop yields and profitability.³¹

A statistical t test was used to analyze the differences between two groups of data. The method compares the mean values of two sets of data and determines if the difference between them is statistically significant. An additional statistical tool, ANOVA, is often known as analysis of variance. ANOVA divides the overall variation in a set of data into two or more elements. All these elements have a particular source of variation attached to it, allowing the analysis to determine how much each of these sources contributed to the overall variation. ANOVA was used most extensively for the analysis of the data obtained from the tests. Both tests are essential in agricultural research for understanding the variation in agricultural elements, including temperature, moisture in the soil, and humidity, and determining the causes that significantly affect these parameters.³² A comparison of the data from different time periods helps to identify the impact of climate change on agricultural parameters and develop crop management systems.

In this work, statistical t tests and ANOVA were performed on the Capsicum crop dataset. Here, the three agricultural parameters of temperature, humidity and soil moisture are considered for the analysis because they are present in plant life cycle events. Understanding the effects of climate change and predicting the impacts of changing phenology on agriculture and resources are important. From the statistical analysis, it is possible to identify the significant differences between the datasets. An autonomous crop management system can be created using this information for the efficient management of agricultural resources.

• t Test:

The statistical t test was carried out using the open source statskingdom tool. A t test was applied to compare the means between the different stages of the crop. This study considered agricultural elements, including temperature, humidity, and soil moisture, at various phases to determine whether applying fertilizer or using an irrigation system has a substantial impact on these parameters.

The t-statistic is described by equation (1).

(1)

t = \frac{(\underline{x_{1}} - \underline{x_{2}}) - {(μ_{1} - μ_{2})}^{0}}{\sqrt{(\frac{s_{p}^{2}}{n_{1}} + \frac{s_{p}^{2}}{n_{2}})}}

where

x₁- sample means of agricultural parameters during phenological stage I

x₂ - sample mean of agricultural parameters during phenological stage II

(μ₁-μ₂)⁰ - Hypothesized differences between the means of the two populations at different phenological stages.

S_P - pooled standard deviation

n_{1 -} Sample size of agricultural parameters during phenological stage I

n₂ - Agricultural parameter sample sizes during phenological stage II

These are the characteristics of the t distribution:

➢ The average is zero.
➢ Around the mean, it is symmetrical.
➢ Between -∞ and ∞ is the variable t’s range.
➢ Since there is a distinct distribution for every sample value of n - 1, the divisor that is used, the t distribution is a collection of variations used to calculate s₂, also known as degrees of freedom.
➢ The t distribution is less centrally peaked and has thicker tails than the normal distribution.
➢ As n approaches infinity, the range of t approaches the normal distribution.

• ANOVA test

The statistical ANOVA test was carried out using an open source statskingdom tool. In this work, the analysis of variance follows a ten-step procedure:

Description of the data. The considered dataset was normalized and classified into different stages of plant phenology. The classified phonological stages of the Capsicum crop are vegetative, flowering, fruiting, and harvesting. The agricultural crop parameters of temperature, humidity and soil moisture were considered for the analysis.

Assumptions: Independency: Independent groups and observations that are representative of the populace.

Normal distribution: The population was distributed uniformly.

Hypothesis: Under the fixed-effects model’s presumptions:

H_{0} : τ_{j} = 0, j = 1, 2, \dots, k

against the alternative

H_{A} : not all τ_{j} = 0

Test statistics: The evaluation metric is the variance ratio (VR).

Statistical distribution test: V.R. has an F distribution when H₀ is true and all the assumptions are satisfied.

Decision rule: If the computed value of the test statistic V.R. is greater than or equal to the crucial value of F, the null hypothesis is rejected.

Test statistic computation: As indicated in Table 2, in this work, there are two components that contribute to the randomized complete block design: the sum of squares between groups (SSB) and the sum of squares of errors (SSE).

Table 2. Randomized complete block design ANOVA table.

Source of Variation	Sum of Squares	Degrees of Freedom	Mean Squares	F Value
Between Groups	SSB= $\sum n_{j} (\bar{X_{j}} - \bar{X)}$ ²	Df₁=k-1	MSB=SSB/(k-1)	F=MSB/MSE
Error	SSE= $\sum \sum (X - \bar{X_{j}})$ ²	Df₂=N-k	MSE=SSE/(N-k)
Total	SST=SSB+SSE	Df₃=N-1

Statistical decision: If the calculated F statistic falls within the rejection range, the null hypothesis H₀ is rejected. The alternate hypothesis H_A is accepted, indicating that the variability present in the data is different for all four months.

Conclusion: If H₀ is rejected, the alternative hypothesis must be true. If H₀ is not rejected, H₀ might be true.

p value determination: For this test, p < 0.05.

The importance of both the t test and ANOVA in agricultural research lies in its ability to help farmers and researchers make data-driven decisions. By conducting both tests, significant differences between crop datasets can be identified. This information can be used for the development of new agricultural technologies for improving agricultural practices.

Results and Discussion

This study’s main goal was to assess how agricultural factors influence the growth and development of Capsicum plants throughout their phenological stages. To investigate this, the t test was employed, a statistical tool that allows for the comparison of means between two or more groups. The primary objective was to determine whether there are statistically significant differences in these agricultural parameters across various phenological stages of Capsicum crop cultivation. The mean and standard deviation (SD) values of the temperatures during the different phenological stages are shown in Table 3. These statistics provide a quantitative understanding of temperature variations throughout crop growth stages for t tests. The mean and standard deviation (SD) values of humidity during the different phenological stages are displayed in Table 4. This data table provides insights into the humidity levels at each stage.

Table 3. The mean and standard deviation scores of the temperatures during the different phenological stages.

Phenological stage	Mean	Standard Deviation (SD)
Vegetative	0.256	0.0653
Flowering	0.263	0.0478
Fruiting	0.2825	0.05796
Harvest	0.3157	0.0630

Table 4. The mean and standard deviation scores of humidity during different phenological stages.

Phenological stage	Mean	Standard Deviation (SD)
Vegetative	0.6208	0.254
Flowering	0.7165	0.2192
Fruiting	0.7236	0.252
Harvest	0.596	0.2795

The mean and standard deviation (SD) values of soil moisture during the different phenological stages are displayed in Table 5. This information can be valuable for making decisions related to crop management and optimizing irrigation practices. The t test analysis of the agricultural parameters of temperature, humidity, and soil moisture across different phenological stages of Capsicum crops provided compelling evidence to support the rejection of the null hypothesis. The t test results are tabulated in Table 6, and Figure 5 shows the t test results.

Table 5. The mean and standard deviation scores of soil moisture during different phenological stages.

Phenological stage	Mean	Standard Deviation (SD)
Vegetative	0.2568	0.1977
Flowering	0.43	0.1985
Fruiting	0.3168	0.201
Harvest	0.2195	0.2143

Table 6. Tabulation of t test results.

Phenology stage	Parameter	p value	T statistic	Null hypothesis test
Vegetative-Flowering	Soil moisture	6.801e-108	-22.9061	rejected
	Temperature	0.002396	-3.0385	rejected
	Humidity	3.987e-24	-10.212	rejected
Flowering-Fruiting	Soil moisture	0	12.7624	rejected
	Temperature	5.858e-16	-8.1595	rejected
	Humidity	0.4997	-0.6751	cannot be rejected
Fruiting-Harvest	Soil moisture	0	12.7959	rejected
	Temperature	6.495e-49	-14.9074	rejected
	Humidity	0	12.9944	rejected

Figure 5. Results of the t test.

With a p value less than the chosen level of significance of 0.05, there were significant differences in the agricultural parameters between the specified phenological stages of the Capsicum crop. These findings underscore the importance of monitoring and managing these parameters throughout the growth cycle of Capsicum plants to optimize their growth and yield. Farmers and researchers can use this valuable insight to make informed decisions and implement targeted strategies for Capsicum cultivation, ultimately contributing to improved agricultural practices and crop productivity.

Analysis of variance (ANOVA) was used to evaluate the variations in temperature, humidity, and soil moisture across the various developmental stages of the Capsicum crop. A significance level (α) of 0.05 was selected as the threshold for determining statistical significance. The ANOVA results are organized and presented in Table 4 for clear reference and interpretation. In this study, we investigated whether there are significant differences in temperature, humidity, and soil moisture levels across different stages of Capsicum crop growth. Table 7 serves as a crucial resource for summarizing the outcomes of the ANOVA. It likely includes key statistical measures such as F values and p values corresponding to each of the three agricultural parameters (temperature, humidity, and soil moisture). These values are essential for making informed decisions regarding the acceptance or rejection of the null hypothesis.

Table 7. ANOVA test results.

Parameter	p value	F statistic	Null hypothesis test
Soil moisture	2.22045e-16	274.031881	rejected
Temperature	0	452.388239	rejected
Humidity	1.22125e-15	95.39	rejected

The results of ANOVA performed on agricultural parameters for different stages of the Capsicum crop are shown in Figure 6. The obtained p values for soil moisture, temperature and humidity were 2.22045e-16, 0 and 1.22125e-15, respectively. The p value is less than the level of significance (α=0.05). There were significant differences among the groups in terms of these parameters. The use of ANOVA in this context enables agricultural scientists, researchers, and farmers to gain valuable insights into how temperature, humidity, and soil moisture influence the various stages of Capsicum crop development. Such knowledge can inform cultivation strategies, help to optimize environmental conditions and ultimately enhance crop yield and quality.

Figure 6. ANOVA results.

Conclusion

The formidable challenge faced by farmers and agriculturalists in analyzing vast amounts of agricultural crop data for resource utilization forecasting has been addressed through the application of open-source statistical tools. The anticipated productivity parameters were identified, contributing to efficient agricultural resource management. Our study specifically scrutinized variations in temperature, humidity, and soil moisture, focusing on Capsicum crop parameters. Through the application of statistical tools such as t tests and ANOVA, significant differences in the parameters were identified across the various phenological stages. The results indicated that temperature, humidity, and soil moisture levels varied notably between stages, underscoring the importance of precise monitoring and management of these factors to optimize crop growth and yield. The use of box plots facilitated the visualization of data variability and outliers, further aiding in decision-making processes.

The statistical findings are instrumental for the development of autonomous crop management systems that can make data-driven decisions. These systems are vital for improving agricultural resource management, enhancing productivity, and promoting sustainability. By leveraging statistical analysis, farmers and researchers can better understand the impacts of climate change and other variables on crop phenology, enabling the adaptation of cultivation practices to changing environmental conditions.

In summary, our research endeavors strive to offer a comprehensive solution to the intricate challenges faced by farmers, providing a pathway to improve crop yield and effective agricultural resource management through the development of an autonomous crop management system.

Author contributions

Conceptualization – Santhosh KV and Deepashri KM; methodology - Deepashri KM, Santhosh KV and J Satheesh Kumar; software – Deepashri KM and Santhosh KV; validation – Santhosh KV and J Satheesh Kumar; formal analysis – Deepashri KM; investigation, Deepashri KM and Santhosh KV; writing original draft preparation – Deepashri KM and J Satheesh Kumar; writing review and editing – Santhosh KV and J Satheesh Kumar; supervision – Santhosh KV.

Ethics and consent

Ethics and consent were not required.

Data availability statement

Open Science Framework: Extended data for ‘capsicum crop growth’ is archived at https://doi.org/10.17605/OSF.IO/WJ295.²⁷

This project contains the following underlying data:

• ‘vegetative_I.csv’ – Sensor data during the process of vegetation
• ‘Flowering_II.csv’ – Sensor data during the process of flowering
• ‘Fruiting_III.csv’ – Sensor data during the process of fruiting
• ‘Harvest_IV.csv’ – Sensor data during the process of harvesting
• ‘ANOVA-test.docx’ – Detail of analysis of data
• ‘Box_plots.docx’ – Results and table of the analysis

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

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Version 2

VERSION 2 PUBLISHED 12 Dec 2024

Author details Author details

Deepashri KM
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation

J Satheesh Kumar
Roles: Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Santhosh KV
Roles: Conceptualization, Investigation, Methodology, Software, Supervision, Validation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

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Published: 07 Jul 2025, 13:1516

https://doi.org/10.12688/f1000research.159518.2

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https://doi.org/10.12688/f1000research.159518.1

© 2025 KM D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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KM D, Satheesh Kumar J and KV S. Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture [version 2; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:1516 (https://doi.org/10.12688/f1000research.159518.2)

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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 2

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PUBLISHED 07 Jul 2025

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Reviewer Report 04 Sep 2025

Dr. Hitesh Kumar Pant, Management, kumaun university Nainital, Bhimtal, uttrakhand, India

Janmejay Pant, Graphic Era Hill University, Dehradun, Uttarakhand, India

Approved

https://doi.org/10.5256/f1000research.184137.r404053

The article “Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture” investigates how environmental parameters (temperature, humidity, and soil moisture) influence Capsicum crops across four phenological stages (vegetative, flowering, fruiting, harvest). Using data collected with Libelium hardware (7,188 samples across four months in 2020), the authors applied statistical methods—t-tests and ANOVA—to identify significant differences across growth stages. The study suggests that these findings can inform autonomous crop management systems, paving the way for improved productivity and sustainability.
The paper is well-structured and cites current literature extensively, covering IoT, fog computing, data mining, machine learning, and crop-specific studies. It contributes by combining phenology, statistical analysis, and agricultural automation, although several areas need improvement before the article can be considered scientifically strong..

The article is generally well-written and references 30+ recent studies (2014–2024), ensuring coverage of modern literature.
However, clarity is weakened by vague terminology in the Abstract and Conclusion (e.g., “substantial variability,” “enhanced productivity”) without quantified results.
Some sections of the literature review are more detailed (IoT, data mining) while others (climate-smart farming) are underdeveloped.
Figures are low quality and occasionally missing (e.g., box plots not well interpreted).

The design (dataset collection, four phenological stages, t-test and ANOVA analysis) is sound and appropriate.
Use of normalization (min-max scaling) is justified for bounded-input algorithms, though alternative approaches (e.g., z-score) could also have been considered.
Statistical significance is reported, but no effect size metrics (Cohen’s d, eta-squared) are provided, limiting interpretation of practical significance.
Dataset limitations (single location, single year) reduce generalizability.

The statistical methods (t-test, ANOVA) are appropriate and correctly applied.
Interpretation focuses mainly on statistical significance (p-values) but omits measures of effect size or biological significance.
Limited discussion of physiological implications (e.g., why soil moisture differs between flowering and harvest, or how this impacts yield).
The results support the claim that environmental parameters vary significantly across Capsicum growth stages.
However, broader claims about “enhanced productivity” and “sustainability” are not directly supported by quantified results.
Biological interpretation and real-world implications (e.g., irrigation or greenhouse automation strategies) are not fully explained.

The article presents a valuable contribution to smart agriculture and crop-specific phenological studies. The study design is sound, the data are openly available, and statistical methods are appropriately applied.
However, before the article can be considered scientifically strong, the following must be addressed:

Quantify results in Abstract and Conclusion; avoid vague terms.
Report effect sizes in addition to p-values.
Improve figure quality and interpretation (especially box plots).
Expand biological/physiological context to explain why variations in temperature, humidity, and soil moisture matter for Capsicum growth.
Acknowledge dataset limitations (single location, short time span).

If these issues are addressed, the paper will be significantly stronger and more impactful for the agricultural technology and crop management research community

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Data Analytics and Predictive Modeling (time-series forecasting, statistical analysis, feature engineering).Deep Learning and Soft Computing (neural networks, statistical modeling, intelligent systems).Artificial Intelligence and Machine Learning (with applications in agriculture, healthcare, and anomaly detection).Agricultural Informatics and Smart Farming Systems (application of AI/ML for precision agriculture, crop management, and environmental monitoring).

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report 26 Aug 2025

Mrutyunjay Padhiary, Assam University, Silchar, Assam, India

Approved with Reservations

https://doi.org/10.5256/f1000research.184137.r397680

This study investigates the impact of key agricultural parameters, such as temperature, humidity, and soil moisture, on the phenological stages of Capsicum cultivation. The authors used statistical hypothesis tests, specifically t-tests and ANOVA, to identify significant differences in these parameters across various growth stages of the Capsicum crop. The research highlights the importance of technology, particularly communication and information advancements, for enhancing farming productivity and efficiency and addressing environmental concerns. By collecting and analyzing vast amounts of agricultural data, researchers can strengthen their knowledge about farming practices and make more informed decisions. The study's findings demonstrate substantial variability in these parameters across phenological stages, which points to the importance of tailored crop management strategies. The ultimate goal is to use these insights to develop autonomous crop management systems that can adapt to specific crop needs, improving agricultural productivity and sustainability.

The statistical analysis and its interpretation are largely appropriate for the stated objectives, demonstrating a sound application of statistical methods to agricultural data. However, there are areas where the interpretation could be strengthened with additional biological context and quantification. The authors should have incorporated biological relevance in revisions and discussed the physiological implications of observed temperature, humidity, and soil moisture variations at each phenological stage.

Addressing dataset constraints, such as being from a single geographic region or lacking multi-year data, could provide a greater understanding of the generalizability of the findings and the scope of the conclusions.

Elaborate on the practical implications for autonomous systems, such as how the identified temperature variability during flowering informs the logic of an automated greenhouse ventilation system or how the soil moisture data drives an intelligent irrigation system that adapts to specific phenological needs.

The study's conclusions are largely supported by the statistical analysis of Capsicum phenology and environmental parameters. The research effectively demonstrates the variability of key parameters across different growth stages and suggests possibilities for autonomous crop management systems based on these findings. The study successfully utilized open-source statistical tools to analyze the Capsicum crop dataset, demonstrating their effectiveness in identifying significant differences and supporting the overall methodology.

Quantification of impact in conclusions should be addressed, as the initial conclusions were noted as broad and lacking specific quantification of the impact of these variations. The conclusion should directly reference the quantified results to support claims of 'enhanced productivity' or 'optimized crop management.' Understanding why certain parameter changes occur and their direct impact on the plant's growth stages would strengthen the causal link between the results and the conclusions regarding optimized crop management.

Improve the quality of the figures. Box plots were mentioned but not fully analyzed in terms of their impact on management decisions. Interpreting trends and anomalies from these plots would provide further insights.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: precision agriculture

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Version 1

VERSION 1

PUBLISHED 12 Dec 2024

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Reviewer Report 28 May 2025

Dua Weraikat, Rochester Institute of Technology-Dubai, Dubai, United Arab Emirates

Approved with Reservations

https://doi.org/10.5256/f1000research.175255.r380097

Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.

The manuscript presents a well-designed study with significant potential for advancing crop-specific automation in agriculture. Addressing methodological transparency, data visualization, and biological interpretation will strengthen its impact. Recommend acceptance after minor revisions.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Smart Agriculture

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Author Response 23 Jul 2025

Santhosh Venkata, Manipal Institute of Technology, Department of Instrumentation and Control Engineering, Manipal Academy of Higher Education, Manipal, India

23 Jul 2025

Author Response

Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on ... Continue reading Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on Capsicum phenology and the application of statistical tools (t-tests and ANOVA) to analyze environmental parameters across growth stages. While IoT and data-driven methods in agriculture are well-researched, the crop-specific approach and emphasis on phenological variability add novelty. The integration of statistical analysis to inform autonomous crop management systems is timely and aligns with precision agriculture trends.
Scope: While the manuscript novelty is in linking Capsicum phenology to statistical analysis for adaptive management, limited discussion of how this work advances beyond existing crop-specific models (e.g., for tomatoes or other crops).
Response: As suggested the study on Capsicum phenology now introduces an approach by integrating statistical analysis specifically t-tests and ANOVA to examine environmental parameters across various growth stages. This provides a statistical foundation for adaptive crop management strategies. In contrast, existing models for other crops, such as tomatoes, have also employed statistical analyses to understand phenology and environmental interactions. However, the Capsicum study's emphasis on integrating statistical tools with real-time environmental data and autonomous management systems represents a notable advancement in the field.

Abstract: Terms like "tailored crop management strategies" and "substantial variability" are vague. Quantifying results (e.g., "temperature increased by X°C during harvest") would strengthen impact. Missing explicit mention of Capsicum in the results (e.g., "Capsicum exhibited significant differences in soil moisture between stages"). Phrases like "statistical hypothesis tests" and "autonomous crop management systems" assume reader familiarity. A brief contextualization (e.g., "statistical tools to compare growth stages") could improve accessibility. Does not clearly state how this work advances beyond prior studies (e.g., "first statistical analysis of Capsicum phenology for automation").
Response: Thanks for the suggestion, the results need to be quantified. This work represents a significant step forward in the systematic study of Capsicum phenology. This offers to build a robust framework for monitoring and managing crop development across varying environmental conditions.

Methodology: The methodology employs a robust dataset (Libellium hardware, 7,188 samples) partitioned into four phenological stages. Statistical tests (t-tests, ANOVA) are appropriate for comparing parameter differences. However, the methodology did not provide the rationale for choosing min-max normalization over alternatives (e.g., z-score). Also, statistical significance (low p-values) is highlighted, but practical significance (e.g., effect size metrics like Cohen’s d) is omitted.
Response: As per the suggestion now the revision is made in the methodology to employ min-max normalization to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for algorithms requiring bounded input such as fuzzy logic, it may not ne optimal datasets with significant outliners.
Regarding statistical significance, the study highlights low p-values, indicating that observed differences are unlikely due to chance. However, it is agreed that p-values alone do not measure the magnitude of these differences. Including effect size metrics, such as Cohen's d, would provide additional context by quantifying the size of the differences between groups will be considered.

Literature Review: The review comprehensively covers IoT, fog computing, and data mining in agriculture, with 30+ references (2014–2024): some sections (e.g., climate-smart farming) are underdeveloped compared to IoT-focused discussions.
Response: Thanks for providing the feed back, we have not tried to review on the climate-smart farming section will be expanded to provide a more balanced perspective on the role of IoT in modern agriculture, addressing both technological advancements and sustainable practices.

Analysis and Results: Results demonstrate significant differences (p < 0.05) in temperature, humidity, and soil moisture across phenological stages. However, no discussion of biological relevance (e.g., why temperature increases during harvest) was provided. Also, the box plots are mentioned but not analyzed (e.g., impact on management decisions).
Response:
The observed environmental variations impact Capsicum physiology and development and their biological relevance will be incorporated in the manuscript.
An interpretation of the box plots, highlighting trends and anomalies across phenological stages will be discussed.

Conclusion: Claims like "pathway to improve crop yield" are broad; linking these directly to the study’s findings (e.g., "soil moisture management during flowering could boost yield by X%") would add rigor.
Response: Such specific findings will be incorporated in the manuscript to provide more precise recommendations for irrigation practices, thereby enhancing its practical relevance for farmers.

Missing Components in conclusion: No discussion of dataset constraints (e.g., single geographic region, lack of multi-year data) and Needs specificity (e.g., "testing the proposed system in field trials" or "extending the model to other Solanaceae crops").
Response: The missing components in conclusion have been addressed. Further the study can provide more comprehensive and actionable insights into Capsicum phenology, contributing to improved agricultural practices and crop management strategies.

Format and Writing Quality: The manuscript follows a standard structure (Abstract, Introduction, Methods, Results, etc.), but several issues detract from clarity:
Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.
Response: The following recommendations will be taken and suggestions will be incorporated in the manuscript.
Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on Capsicum phenology and the application of statistical tools (t-tests and ANOVA) to analyze environmental parameters across growth stages. While IoT and data-driven methods in agriculture are well-researched, the crop-specific approach and emphasis on phenological variability add novelty. The integration of statistical analysis to inform autonomous crop management systems is timely and aligns with precision agriculture trends.
Scope: While the manuscript novelty is in linking Capsicum phenology to statistical analysis for adaptive management, limited discussion of how this work advances beyond existing crop-specific models (e.g., for tomatoes or other crops).
Response: As suggested the study on Capsicum phenology now introduces an approach by integrating statistical analysis specifically t-tests and ANOVA to examine environmental parameters across various growth stages. This provides a statistical foundation for adaptive crop management strategies. In contrast, existing models for other crops, such as tomatoes, have also employed statistical analyses to understand phenology and environmental interactions. However, the Capsicum study's emphasis on integrating statistical tools with real-time environmental data and autonomous management systems represents a notable advancement in the field.

Abstract: Terms like "tailored crop management strategies" and "substantial variability" are vague. Quantifying results (e.g., "temperature increased by X°C during harvest") would strengthen impact. Missing explicit mention of Capsicum in the results (e.g., "Capsicum exhibited significant differences in soil moisture between stages"). Phrases like "statistical hypothesis tests" and "autonomous crop management systems" assume reader familiarity. A brief contextualization (e.g., "statistical tools to compare growth stages") could improve accessibility. Does not clearly state how this work advances beyond prior studies (e.g., "first statistical analysis of Capsicum phenology for automation").
Response: Thanks for the suggestion, the results need to be quantified. This work represents a significant step forward in the systematic study of Capsicum phenology. This offers to build a robust framework for monitoring and managing crop development across varying environmental conditions.

Methodology: The methodology employs a robust dataset (Libellium hardware, 7,188 samples) partitioned into four phenological stages. Statistical tests (t-tests, ANOVA) are appropriate for comparing parameter differences. However, the methodology did not provide the rationale for choosing min-max normalization over alternatives (e.g., z-score). Also, statistical significance (low p-values) is highlighted, but practical significance (e.g., effect size metrics like Cohen’s d) is omitted.
Response: As per the suggestion now the revision is made in the methodology to employ min-max normalization to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for algorithms requiring bounded input such as fuzzy logic, it may not ne optimal datasets with significant outliners.
Regarding statistical significance, the study highlights low p-values, indicating that observed differences are unlikely due to chance. However, it is agreed that p-values alone do not measure the magnitude of these differences. Including effect size metrics, such as Cohen's d, would provide additional context by quantifying the size of the differences between groups will be considered.

Literature Review: The review comprehensively covers IoT, fog computing, and data mining in agriculture, with 30+ references (2014–2024): some sections (e.g., climate-smart farming) are underdeveloped compared to IoT-focused discussions.
Response: Thanks for providing the feed back, we have not tried to review on the climate-smart farming section will be expanded to provide a more balanced perspective on the role of IoT in modern agriculture, addressing both technological advancements and sustainable practices.

Analysis and Results: Results demonstrate significant differences (p < 0.05) in temperature, humidity, and soil moisture across phenological stages. However, no discussion of biological relevance (e.g., why temperature increases during harvest) was provided. Also, the box plots are mentioned but not analyzed (e.g., impact on management decisions).
Response:
The observed environmental variations impact Capsicum physiology and development and their biological relevance will be incorporated in the manuscript.
An interpretation of the box plots, highlighting trends and anomalies across phenological stages will be discussed.

Conclusion: Claims like "pathway to improve crop yield" are broad; linking these directly to the study’s findings (e.g., "soil moisture management during flowering could boost yield by X%") would add rigor.
Response: Such specific findings will be incorporated in the manuscript to provide more precise recommendations for irrigation practices, thereby enhancing its practical relevance for farmers.

Missing Components in conclusion: No discussion of dataset constraints (e.g., single geographic region, lack of multi-year data) and Needs specificity (e.g., "testing the proposed system in field trials" or "extending the model to other Solanaceae crops").
Response: The missing components in conclusion have been addressed. Further the study can provide more comprehensive and actionable insights into Capsicum phenology, contributing to improved agricultural practices and crop management strategies.

Format and Writing Quality: The manuscript follows a standard structure (Abstract, Introduction, Methods, Results, etc.), but several issues detract from clarity:
Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.
Response: The following recommendations will be taken and suggestions will be incorporated in the manuscript.
Competing Interests: Author declare no competing interests Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 23 Jul 2025

Santhosh Venkata, Manipal Institute of Technology, Department of Instrumentation and Control Engineering, Manipal Academy of Higher Education, Manipal, India

23 Jul 2025

Author Response

Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on ... Continue reading Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on Capsicum phenology and the application of statistical tools (t-tests and ANOVA) to analyze environmental parameters across growth stages. While IoT and data-driven methods in agriculture are well-researched, the crop-specific approach and emphasis on phenological variability add novelty. The integration of statistical analysis to inform autonomous crop management systems is timely and aligns with precision agriculture trends.
Scope: While the manuscript novelty is in linking Capsicum phenology to statistical analysis for adaptive management, limited discussion of how this work advances beyond existing crop-specific models (e.g., for tomatoes or other crops).
Response: As suggested the study on Capsicum phenology now introduces an approach by integrating statistical analysis specifically t-tests and ANOVA to examine environmental parameters across various growth stages. This provides a statistical foundation for adaptive crop management strategies. In contrast, existing models for other crops, such as tomatoes, have also employed statistical analyses to understand phenology and environmental interactions. However, the Capsicum study's emphasis on integrating statistical tools with real-time environmental data and autonomous management systems represents a notable advancement in the field.

Abstract: Terms like "tailored crop management strategies" and "substantial variability" are vague. Quantifying results (e.g., "temperature increased by X°C during harvest") would strengthen impact. Missing explicit mention of Capsicum in the results (e.g., "Capsicum exhibited significant differences in soil moisture between stages"). Phrases like "statistical hypothesis tests" and "autonomous crop management systems" assume reader familiarity. A brief contextualization (e.g., "statistical tools to compare growth stages") could improve accessibility. Does not clearly state how this work advances beyond prior studies (e.g., "first statistical analysis of Capsicum phenology for automation").
Response: Thanks for the suggestion, the results need to be quantified. This work represents a significant step forward in the systematic study of Capsicum phenology. This offers to build a robust framework for monitoring and managing crop development across varying environmental conditions.

Methodology: The methodology employs a robust dataset (Libellium hardware, 7,188 samples) partitioned into four phenological stages. Statistical tests (t-tests, ANOVA) are appropriate for comparing parameter differences. However, the methodology did not provide the rationale for choosing min-max normalization over alternatives (e.g., z-score). Also, statistical significance (low p-values) is highlighted, but practical significance (e.g., effect size metrics like Cohen’s d) is omitted.
Response: As per the suggestion now the revision is made in the methodology to employ min-max normalization to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for algorithms requiring bounded input such as fuzzy logic, it may not ne optimal datasets with significant outliners.
Regarding statistical significance, the study highlights low p-values, indicating that observed differences are unlikely due to chance. However, it is agreed that p-values alone do not measure the magnitude of these differences. Including effect size metrics, such as Cohen's d, would provide additional context by quantifying the size of the differences between groups will be considered.

Literature Review: The review comprehensively covers IoT, fog computing, and data mining in agriculture, with 30+ references (2014–2024): some sections (e.g., climate-smart farming) are underdeveloped compared to IoT-focused discussions.
Response: Thanks for providing the feed back, we have not tried to review on the climate-smart farming section will be expanded to provide a more balanced perspective on the role of IoT in modern agriculture, addressing both technological advancements and sustainable practices.

Analysis and Results: Results demonstrate significant differences (p < 0.05) in temperature, humidity, and soil moisture across phenological stages. However, no discussion of biological relevance (e.g., why temperature increases during harvest) was provided. Also, the box plots are mentioned but not analyzed (e.g., impact on management decisions).
Response:
The observed environmental variations impact Capsicum physiology and development and their biological relevance will be incorporated in the manuscript.
An interpretation of the box plots, highlighting trends and anomalies across phenological stages will be discussed.

Conclusion: Claims like "pathway to improve crop yield" are broad; linking these directly to the study’s findings (e.g., "soil moisture management during flowering could boost yield by X%") would add rigor.
Response: Such specific findings will be incorporated in the manuscript to provide more precise recommendations for irrigation practices, thereby enhancing its practical relevance for farmers.

Missing Components in conclusion: No discussion of dataset constraints (e.g., single geographic region, lack of multi-year data) and Needs specificity (e.g., "testing the proposed system in field trials" or "extending the model to other Solanaceae crops").
Response: The missing components in conclusion have been addressed. Further the study can provide more comprehensive and actionable insights into Capsicum phenology, contributing to improved agricultural practices and crop management strategies.

Format and Writing Quality: The manuscript follows a standard structure (Abstract, Introduction, Methods, Results, etc.), but several issues detract from clarity:
Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.
Response: The following recommendations will be taken and suggestions will be incorporated in the manuscript.
Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on Capsicum phenology and the application of statistical tools (t-tests and ANOVA) to analyze environmental parameters across growth stages. While IoT and data-driven methods in agriculture are well-researched, the crop-specific approach and emphasis on phenological variability add novelty. The integration of statistical analysis to inform autonomous crop management systems is timely and aligns with precision agriculture trends.
Scope: While the manuscript novelty is in linking Capsicum phenology to statistical analysis for adaptive management, limited discussion of how this work advances beyond existing crop-specific models (e.g., for tomatoes or other crops).
Response: As suggested the study on Capsicum phenology now introduces an approach by integrating statistical analysis specifically t-tests and ANOVA to examine environmental parameters across various growth stages. This provides a statistical foundation for adaptive crop management strategies. In contrast, existing models for other crops, such as tomatoes, have also employed statistical analyses to understand phenology and environmental interactions. However, the Capsicum study's emphasis on integrating statistical tools with real-time environmental data and autonomous management systems represents a notable advancement in the field.

Abstract: Terms like "tailored crop management strategies" and "substantial variability" are vague. Quantifying results (e.g., "temperature increased by X°C during harvest") would strengthen impact. Missing explicit mention of Capsicum in the results (e.g., "Capsicum exhibited significant differences in soil moisture between stages"). Phrases like "statistical hypothesis tests" and "autonomous crop management systems" assume reader familiarity. A brief contextualization (e.g., "statistical tools to compare growth stages") could improve accessibility. Does not clearly state how this work advances beyond prior studies (e.g., "first statistical analysis of Capsicum phenology for automation").
Response: Thanks for the suggestion, the results need to be quantified. This work represents a significant step forward in the systematic study of Capsicum phenology. This offers to build a robust framework for monitoring and managing crop development across varying environmental conditions.

Methodology: The methodology employs a robust dataset (Libellium hardware, 7,188 samples) partitioned into four phenological stages. Statistical tests (t-tests, ANOVA) are appropriate for comparing parameter differences. However, the methodology did not provide the rationale for choosing min-max normalization over alternatives (e.g., z-score). Also, statistical significance (low p-values) is highlighted, but practical significance (e.g., effect size metrics like Cohen’s d) is omitted.
Response: As per the suggestion now the revision is made in the methodology to employ min-max normalization to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for algorithms requiring bounded input such as fuzzy logic, it may not ne optimal datasets with significant outliners.
Regarding statistical significance, the study highlights low p-values, indicating that observed differences are unlikely due to chance. However, it is agreed that p-values alone do not measure the magnitude of these differences. Including effect size metrics, such as Cohen's d, would provide additional context by quantifying the size of the differences between groups will be considered.

Literature Review: The review comprehensively covers IoT, fog computing, and data mining in agriculture, with 30+ references (2014–2024): some sections (e.g., climate-smart farming) are underdeveloped compared to IoT-focused discussions.
Response: Thanks for providing the feed back, we have not tried to review on the climate-smart farming section will be expanded to provide a more balanced perspective on the role of IoT in modern agriculture, addressing both technological advancements and sustainable practices.

Analysis and Results: Results demonstrate significant differences (p < 0.05) in temperature, humidity, and soil moisture across phenological stages. However, no discussion of biological relevance (e.g., why temperature increases during harvest) was provided. Also, the box plots are mentioned but not analyzed (e.g., impact on management decisions).
Response:
The observed environmental variations impact Capsicum physiology and development and their biological relevance will be incorporated in the manuscript.
An interpretation of the box plots, highlighting trends and anomalies across phenological stages will be discussed.

Conclusion: Claims like "pathway to improve crop yield" are broad; linking these directly to the study’s findings (e.g., "soil moisture management during flowering could boost yield by X%") would add rigor.
Response: Such specific findings will be incorporated in the manuscript to provide more precise recommendations for irrigation practices, thereby enhancing its practical relevance for farmers.

Missing Components in conclusion: No discussion of dataset constraints (e.g., single geographic region, lack of multi-year data) and Needs specificity (e.g., "testing the proposed system in field trials" or "extending the model to other Solanaceae crops").
Response: The missing components in conclusion have been addressed. Further the study can provide more comprehensive and actionable insights into Capsicum phenology, contributing to improved agricultural practices and crop management strategies.

Format and Writing Quality: The manuscript follows a standard structure (Abstract, Introduction, Methods, Results, etc.), but several issues detract from clarity:
Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.
Response: The following recommendations will be taken and suggestions will be incorporated in the manuscript.
Competing Interests: Author declare no competing interests Close
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VERSION 2 PUBLISHED 12 Dec 2024

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Version 2 (revision) 07 Jul 25		read	read
Version 1 12 Dec 24	read

Dua Weraikat, Rochester Institute of Technology-Dubai, Dubai, United Arab Emirates
Mrutyunjay Padhiary, Assam University, Silchar, India
Dr. Hitesh Kumar Pant, kumaun university Nainital, Bhimtal, India

Janmejay Pant, Graphic Era Hill University, Dehradun, India

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Reviewer Report

4 Views

04 Sep 2025 | for Version 2

Dr. Hitesh Kumar Pant, Management, kumaun university Nainital, Bhimtal, uttrakhand, India

Janmejay Pant, Graphic Era Hill University, Dehradun, Uttarakhand, India

4 Views Cite this report Responses(0)

Approved

The article is generally well-written and references 30+ recent studies (2014–2024), ensuring coverage of modern literature.
However, clarity is weakened by vague terminology in the Abstract and Conclusion (e.g., “substantial variability,” “enhanced productivity”) without quantified results.
Some sections of the literature review are more detailed (IoT, data mining) while others (climate-smart farming) are underdeveloped.
Figures are low quality and occasionally missing (e.g., box plots not well interpreted).

The design (dataset collection, four phenological stages, t-test and ANOVA analysis) is sound and appropriate.
Use of normalization (min-max scaling) is justified for bounded-input algorithms, though alternative approaches (e.g., z-score) could also have been considered.
Statistical significance is reported, but no effect size metrics (Cohen’s d, eta-squared) are provided, limiting interpretation of practical significance.
Dataset limitations (single location, single year) reduce generalizability.

The statistical methods (t-test, ANOVA) are appropriate and correctly applied.
Interpretation focuses mainly on statistical significance (p-values) but omits measures of effect size or biological significance.
Limited discussion of physiological implications (e.g., why soil moisture differs between flowering and harvest, or how this impacts yield).
The results support the claim that environmental parameters vary significantly across Capsicum growth stages.
However, broader claims about “enhanced productivity” and “sustainability” are not directly supported by quantified results.
Biological interpretation and real-world implications (e.g., irrigation or greenhouse automation strategies) are not fully explained.

Quantify results in Abstract and Conclusion; avoid vague terms.
Report effect sizes in addition to p-values.
Improve figure quality and interpretation (especially box plots).
Expand biological/physiological context to explain why variations in temperature, humidity, and soil moisture matter for Capsicum growth.
Acknowledge dataset limitations (single location, short time span).

If these issues are addressed, the paper will be significantly stronger and more impactful for the agricultural technology and crop management research community

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Data Analytics and Predictive Modeling (time-series forecasting, statistical analysis, feature engineering).Deep Learning and Soft Computing (neural networks, statistical modeling, intelligent systems).Artificial Intelligence and Machine Learning (with applications in agriculture, healthcare, and anomaly detection).Agricultural Informatics and Smart Farming Systems (application of AI/ML for precision agriculture, crop management, and environmental monitoring).

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

4 Views

26 Aug 2025 | for Version 2

Mrutyunjay Padhiary, Assam University, Silchar, Assam, India

4 Views Cite this report Responses(0)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

precision agriculture

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Reviewer Report

10 Views

28 May 2025 | for Version 1

Dua Weraikat, Rochester Institute of Technology-Dubai, Dubai, United Arab Emirates

10 Views Cite this report Responses(1)

Approved With Reservations

Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Smart Agriculture

Respond to this report

Responses (1)

Author Response

23 Jul 2025

Santhosh Venkata, Manipal Institute of Technology, Department of Instrumentation and Control Engineering, Manipal Academy of Higher Education, Manipal, India

Peer-Review Report: "Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture"
The study addresses a relevant gap in smart agriculture by focusing on Capsicum phenology and the application of statistical tools (t-tests and ANOVA) to analyze environmental parameters across growth stages. While IoT and data-driven methods in agriculture are well-researched, the crop-specific approach and emphasis on phenological variability add novelty. The integration of statistical analysis to inform autonomous crop management systems is timely and aligns with precision agriculture trends.
Scope: While the manuscript novelty is in linking Capsicum phenology to statistical analysis for adaptive management, limited discussion of how this work advances beyond existing crop-specific models (e.g., for tomatoes or other crops).
Response: As suggested the study on Capsicum phenology now introduces an approach by integrating statistical analysis specifically t-tests and ANOVA to examine environmental parameters across various growth stages. This provides a statistical foundation for adaptive crop management strategies. In contrast, existing models for other crops, such as tomatoes, have also employed statistical analyses to understand phenology and environmental interactions. However, the Capsicum study's emphasis on integrating statistical tools with real-time environmental data and autonomous management systems represents a notable advancement in the field.

Abstract: Terms like "tailored crop management strategies" and "substantial variability" are vague. Quantifying results (e.g., "temperature increased by X°C during harvest") would strengthen impact. Missing explicit mention of Capsicum in the results (e.g., "Capsicum exhibited significant differences in soil moisture between stages"). Phrases like "statistical hypothesis tests" and "autonomous crop management systems" assume reader familiarity. A brief contextualization (e.g., "statistical tools to compare growth stages") could improve accessibility. Does not clearly state how this work advances beyond prior studies (e.g., "first statistical analysis of Capsicum phenology for automation").
Response: Thanks for the suggestion, the results need to be quantified. This work represents a significant step forward in the systematic study of Capsicum phenology. This offers to build a robust framework for monitoring and managing crop development across varying environmental conditions.

Methodology: The methodology employs a robust dataset (Libellium hardware, 7,188 samples) partitioned into four phenological stages. Statistical tests (t-tests, ANOVA) are appropriate for comparing parameter differences. However, the methodology did not provide the rationale for choosing min-max normalization over alternatives (e.g., z-score). Also, statistical significance (low p-values) is highlighted, but practical significance (e.g., effect size metrics like Cohen’s d) is omitted.
Response: As per the suggestion now the revision is made in the methodology to employ min-max normalization to scale environmental parameters across four Capsicum phenological stages. While this approach is suitable for algorithms requiring bounded input such as fuzzy logic, it may not ne optimal datasets with significant outliners.
Regarding statistical significance, the study highlights low p-values, indicating that observed differences are unlikely due to chance. However, it is agreed that p-values alone do not measure the magnitude of these differences. Including effect size metrics, such as Cohen's d, would provide additional context by quantifying the size of the differences between groups will be considered.

Literature Review: The review comprehensively covers IoT, fog computing, and data mining in agriculture, with 30+ references (2014–2024): some sections (e.g., climate-smart farming) are underdeveloped compared to IoT-focused discussions.
Response: Thanks for providing the feed back, we have not tried to review on the climate-smart farming section will be expanded to provide a more balanced perspective on the role of IoT in modern agriculture, addressing both technological advancements and sustainable practices.

Analysis and Results: Results demonstrate significant differences (p < 0.05) in temperature, humidity, and soil moisture across phenological stages. However, no discussion of biological relevance (e.g., why temperature increases during harvest) was provided. Also, the box plots are mentioned but not analyzed (e.g., impact on management decisions).
Response:
The observed environmental variations impact Capsicum physiology and development and their biological relevance will be incorporated in the manuscript.
An interpretation of the box plots, highlighting trends and anomalies across phenological stages will be discussed.

Conclusion: Claims like "pathway to improve crop yield" are broad; linking these directly to the study’s findings (e.g., "soil moisture management during flowering could boost yield by X%") would add rigor.
Response: Such specific findings will be incorporated in the manuscript to provide more precise recommendations for irrigation practices, thereby enhancing its practical relevance for farmers.

Missing Components in conclusion: No discussion of dataset constraints (e.g., single geographic region, lack of multi-year data) and Needs specificity (e.g., "testing the proposed system in field trials" or "extending the model to other Solanaceae crops").
Response: The missing components in conclusion have been addressed. Further the study can provide more comprehensive and actionable insights into Capsicum phenology, contributing to improved agricultural practices and crop management strategies.

Format and Writing Quality: The manuscript follows a standard structure (Abstract, Introduction, Methods, Results, etc.), but several issues detract from clarity:
Typos: Inconsistent use of "Capsicum" (e.g., "Cap sicum" on Page 9).
Figures/Tables: Referenced but absent (e.g., Figures 1–3, Table 1), limiting reproducibility assessment.
Date anomalies: Publication dates (Dec 2024) are future-dated, likely a formatting error.
Figures Quality: Figures like Fig 1 and Fig 5 quality are poor and need to be enhanced.
Response: The following recommendations will be taken and suggestions will be incorporated in the manuscript.

View more View less

Competing Interests

Author declare no competing interests

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Harnessing Technological Advancements for Enhanced Crop Management: A Study on Capsicum Phenology and Automation in Agriculture

Abstract

Background

Methods

Results

Conclusion

Keywords

Revised Amendments from Version 1

Introduction

Literature survey

Method

Table 1. Phenological stages of the Capsicum crop.

Figure 1. Box plot of the temperature data.

Figure 2. Box plot of the humidity data.

Figure 3. Box plot of the soil moisture data.

Figure 4. Methodology for the statistical analysis of agricultural parameters.

Statistical analysis

(1)

Table 2. Randomized complete block design ANOVA table.

Results and Discussion

Table 3. The mean and standard deviation scores of the temperatures during the different phenological stages.

Table 4. The mean and standard deviation scores of humidity during different phenological stages.

Table 5. The mean and standard deviation scores of soil moisture during different phenological stages.

Table 6. Tabulation of t test results.

Figure 5. Results of the t test.

Table 7. ANOVA test results.

Figure 6. ANOVA results.

Conclusion

Author contributions

Ethics and consent

Data availability statement

References

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