Proposed Solar-Powered Motion Sensor for Farm Monitoring and Surveillance: A Solar-Powered Assisted Process

1.1 Research gap

The current solution for using motion sensor technologies and implementing them in farm monitoring systems is also not without several drawbacks that hamper their efficacy, especially for the large-scale and remote environments of the farming sector. Almost all the conventional motion sensors and CCTV cameras are specially designed to detect human movement or larger animal intrusions but are not efficient at detecting smaller vertebrate pests like rodents that are well known to cause massive losses through crop and stored produce raiding. This consequently results in huge losses since rodent infestations are normally undetected until extensive damage has been done. Moreover, the current surveillance systems require a constant supply of electrical power, which is not available in most of the rural and developed central region areas.

Another problem in current motion sensor systems is that they are not real-time responsive, or at least not fully automated. Most typical systems employ the human operator to watch monitors for the footage or to respond to alerts, which raises a great deal of labor costs and hampers the efficiency of farm surveillance. All these systems are post-incident systems, which implies that these systems work following an incident, not to prevent it. Also, the utilization of these energy resources is costly and raises the environmental costs of operation, thereby making these systems unsustainable in their current utilization.

Hence, the research question lies in finding a solution that would not only enhance the detection accuracy of relatively small targets like rodents but also employ sustainable forms of power in the process. The resulting gaps can be filled by the proposed solar-powered motion sensor system, which is capable of using renewable energy, up-to-date sensors, automation, and systems to monitor both large and small intrusions more effectively in terms of energy consumption.

1.2 Research aim and objectives

The primary objective of this proposed study is to develop a solar-powered motion sensor system capable of recognizing rodent invasion on farms within the monitored area, thereby enhancing efficient monitoring and surveillance efforts while consuming energy efficiently.

1.3 Research question

The proposed study is guided by the following research question:

1.4 Research novelty

This study proposes a new concept of farm monitoring and surveillance through the use of solar-powered motion sensors coupled with computer vision and deep learning algorithms for rodent invasion detection. Compared to conventional farm surveillance systems, which are primarily meant to monitor human interference or large animal movement and require continuous electrical power, this proposed system is intended to be energy-autonomous and is to be powered through solar energy. This not only provides for the need to power such devices in areas where there is no physical access to an energy grid but also provides a positive impact on worldwide sustainability as the need for fossil fuels lessens.

Furthermore, through the integrated feature of computer vision technology, it also has the possibility to differentiate between varying cycles of motion, for instance, to differentiate the wind-blown leaves with rodent movements. The use of deep learning in the algorithms helps the system to update the information that is collected, enhancing detection results in the future. This adaptive capability greatly improves the chances of the system to detect the smaller pests that are ignored in most traditional systems. Further, notification through a mobile application that reflects the actual situation in the field enables farmers to act instantly and avoid further harm that may require manual intervention.

This study’s uniqueness is found in the integration of renewable energy sources, advanced detection systems, and automation in the monitoring of farms. This approach has the possibility of radically changing the practice of monitoring farms, providing one distinctive tool for the farmers, especially those who are located in areas where they have no access to the normal electrical power source.

2.1 Monitoring systems for farms utilizing sensors

The use of sensors has been vastly studied for the protection of agricultural farms and for detecting possible intruders as soon as possible. Mrunal Khedkar (2021) proposed a system that employs wireless sensor nodes in conjunction with passive infrared (PIR) sensors and low-power digital cameras that can detect motion and take pictures of intruders. The PIR sensors, universal in motion detection, are sensitive to variations in infrared emissions that warm objects produce. On detection, a low-power camera, including the OpenMV Cam, is actuated to take pictures or record the video. These visual data are transmitted wirelessly through protocols that include ZigBee or Bluetooth, whereby long-range protocols, such as the ZigBee, are appropriate for large areas of the farm, and short-range protocols, such as the Bluetooth. The base station collects the images and analyses them to capture the intruder; the quality of images is measured using peak signal-to-noise ratio (PSNR) and mean squared error (MSE).

Nevertheless, this system is a good example of PIR sensors and low-power cameras used in farm monitoring, though the whole system remains dependent on external power supplies. Its use is slightly effective in remote farm areas where power availability might be quite a challenge. Furthermore, using conventional battery-based devices for the wireless sensor nodes may not extend for long hours of performance without requiring frequent charging or replacement of batteries with fresh ones.

Sowmika, Rohith Paul, and Malathi (2020) described an Internet of Things (IoT)-based rodent detection system using a PIR sensor for detecting the rodent. This system is triggered by sensing infrared radiation emitted from the body of the rats and sends an alert to a cloud-based platform. The PIR sensor for the rodent location works up to a 10-meter range, and the mode of operation can be live with instant alerts to the farmer via mobile applications. This system gives real-time detection and notification through wireless means, but it has a limited range and, more critically, it derives its power from conventional sources, thus a constraint in rural farming environments not connected to the grid.

2.2 The application of vision-based farm surveillance system

Vision-based systems are now considered an essential part of today’s farm monitoring and surveillance because of their capacity to obtain and interpret visual information. A low-power bait station monitoring system for rodent detection has been designed by Ross et al. (2020), and it is known as RatSpy. RatSpy employs a combination of sensors and cameras to detect rats’ movement and the uptake of baits without the need for inspection. The results are sent wirelessly to pest control operators, enabling those operators to monitor the status without physically checking it. Owing to technology, the labor expenses associated with monitoring the bait stations’ condition and the general rodent population have greatly decreased, and the best feature of this solution is that it always actively scans for rodents. Nevertheless, the system has consistently lower power consumption to accomplish its task, which realistically necessitates battery change, making it impractical for large-scale farming or areas where a simple battery replacement is not easily accessible.

Lai et al. (2023) offered an ingenious approach using IoT nodes with Long Range (LoRa) modules to look after the farm. These nodes are fitted with PIR sensors and an ESP32 camera for picture capture in real time. The system provided with a terminal transmits the data on the rodent activity wirelessly and stores it at a cloud server to process and analyze. The flexibility offered by LoRa technology of transmitting information from one IoT node to another and to the cloud server is well applicable to large farms. However, there is the same problem known for other vision-based systems: energy consumption is high due to the continuous work of cameras and sensors that need a stable power supply.

Implementation of the deep learning algorithms in these systems was likely to provide more precise detection of the mice, for instance, as an individual threat within the building. Nevertheless, the utilization of deep learning models entails higher computational requirements that may further exponentially drain the energy supply, requiring a more efficient power source.

2.3 Wireless sensor networks and low-power solutions

Wireless sensor networks (WSNs) capable of operating at low power have been researched with the view to extending the battery lifetime of the farm monitoring systems and, as a result, minimizing the need for frequent battery replacement. In that recent work, Cambra et al. (2017) proposed another low-power WSN system that employs a multi-hop wireless mesh network design for detecting rodent pests in agricultural fields. The system comprises the network coordinator, parent nodes – routers, and child nodes – sensor nodes. The PIR motion sensors placed on the hardware of the sensor nodes detect the motion and relay the information received through the mesh network to the network coordinator. The data transmission is made utilizing nRF24L01+ 2.4 GHz RF Transceivers, which consume negligible power. Besides, the system utilizes a number of power management features, including power down, interrupt, and sleep, which all help to further prolong battery lifespan.

Though this system provides up to 90% saving of energy use, it comes from the conventional battery power that needs to be recharged. However, the sensitivity level of PIR sensors is not very high; we need to go for the fine level of detecting small creatures like rats, etc.

2.4 Solar-powered monitoring systems

Solar power has gained traction as an alternative energy source for farm surveillance systems, particularly in remote areas where access to the electrical grid is limited. Patel et al. (2021) explored a solar-powered IoT-based agricultural monitoring system that integrates solar panels to power wireless sensor networks and GSM communication devices. This approach significantly reduces reliance on conventional power sources and enhances the system’s feasibility in off-grid locations. The system monitors crop health, soil moisture, and environmental conditions using IoT sensors and transmits data wirelessly to the farmer’s mobile device. However, while this system addresses power-related challenges, it is not optimized for rodent detection or other farm surveillance needs, as its primary focus is on crop monitoring.

Adirala et al. (2025) devised an IoT-based detection system using PIR motion sensors to detect intrusions from large animals, such as cattle. The system employs a two-level deterrent mechanism namely randomized carnivorous animal sounds and high-intensity focus lights, and wireless connectivity for remote monitoring. However, it is limited to detecting larger animals and is not sensitive enough for small pests like rodents. Thus, a more advanced detection mechanism is required to monitor a wider range of potential animal intruders.

3.1 System infrastructure

Figures 1 and 2 comprise the system infrastructure based on a motion sensor, flood light, motor, battery, speaker, rotating knob, a smart camera, and a solar panel.

Figure 1. Front view of the system.

Figure 2. Top (left) and back (right) view of the system.

3.2 System components

3.2.1 Motion sensor

Image-based motion sensors analyze changes in the captured video to visually identify movement using cameras and computer vision algorithms (Futagami et al., 2020). They are extremely accurate for this system because they use sophisticated algorithms for motion pattern detection and object tracking.

3.2.2 Data transmission protocol

WI-FI data transmission protocols will enable data transfer between the system and the mobile application. WI-FI allows the transfer of data over a wide range (Pahlavan and Krishnamurthy, 2020), hence the most convenient for this type of technology. When the system detects motion and the camera takes a video, and processes it, it will use WI-FI to notify the farmer of the intrusion.

3.2.3 Rotating knob

The rotating knob serves as a mechanism to physically rotate the entire surveillance equipment, allowing the smart camera to capture images from all angles within the monitored area. It is connected to a motorized rotational mechanism, which rotates the entire surveillance equipment horizontally. Users can rotate the knob manually to adjust the viewing angle of the surveillance equipment.

It is integrated with the battery to give power to the motorized rotational mechanism and motion detection sensors to enable it to pause temporarily when motion is detected, ensuring that the camera focuses on the detected movement to capture images or video footage.

3.2.4 Smart camera

According to Kurniawan and Sofiarani (2020), a smart camera combines complex image sensors and processors with the capability to process images on its own without assistance from humans. It records high-quality video footage of the areas under observation only once the motion is detected, enabling remote surveillance, evidence collection, and in-depth analysis of any activity that is detected.

Smart cameras are equipped with high-resolution imaging sensors that enable them to record clear and detailed video footage. These sensors might use technologies like charge-coupled devices (CCD) or complementary metal-oxide semiconductors (CMOS), which provide better image quality even in low light.

3.2.5 Flood light

Floodlight is the essential element that produces light illumination in low-light or nighttime conditions. It typically uses energy-efficient LED (Light-Emitting Diode) technology. LEDs deliver a high light output while using very little power energy, making them ideal for solar-powered systems where energy conservation is essential (Pulli et al., 2015). The floodlight is connected to the solar-charged battery, enabling it to draw power from it and operate effectively at night. This component is particularly useful for farm surveillance, as it can deter potential intruders and help the smart camera identify any activity occurring after dark.

3.2.6 Battery

The battery serves as the energy storage component in the system, ensuring a reliable and continuous power supply for the motion sensor, floodlight, and any other associated components. The preferred battery type for this system is lithium-ion batteries because they can be frequently charged and discharged without experiencing significant degradation and can be used for extended periods of time in solar-powered applications (Manthiram, 2017). All linked components receive a smooth power supply from the battery as it is integrated into the overall system architecture.

3.2.7 Speaker

The speaker serves as an audio output device within the surveillance system. Its main purpose is to mimic sound, which allows for alarming and alerting. According to Bernardini, Bianchi and Sarti (2023), speakers utilize transducer technology to change electrical signals into sound waves, which are then released by speakers. When these transducers receive electrical signals, they vibrate, which causes sound waves to travel through the atmosphere.

The motion sensor and speaker are combined so that when motion is detected, the motion sensor sends a signal to close the circuit linking the speakers. This causes the speakers to activate and emit sound alarms to frighten away any intruders, which includes either livestock or rodents.

4.1 Image recognition process in OpenCV

Preprocessing techniques are first used to simplify and lower noise in the image, as shown in Figure 3. These techniques include first grayscale conversion and then Gaussian blurring. After that, thresholding is done to create a binary image that highlights the important objects. This binary image has contours that show the edges of distinct objects. Each contour’s area is computed, and contours having areas outside of a predetermined range are removed. For each remaining contour, bounding rectangles are generated in order to identify and pinpoint each particular object in the image (Duwal and Tamang, 2024).

Figure 3. Flowchart showing image processing steps.

The following analytical results were obtained from the training and testing of the model.

4.2 Accuracy per class of rodents

The accuracy per class of rodents is calculated (see Figure 4) using the test samples. The test samples include 15% of the samples that are not used in model training. Therefore, after the model has been trained, the model uses these samples to test performance on new data.

Figure 4. Accuracy per class of rodents.

4.3 Confusion matrix

Figure 5 shows the confusion matrix between classes of the model. This summarizes the accuracy of the model in determining confusion between samples in a class and that of another class. The class of the samples is shown on the y-axis, whereas the x-axis (prediction) shows the class to which the model classifies the samples to belong after learning. For instance, from the above results, it can be shown that after learning from the data, the model misclassified one instance of the rat class as a mouse. This means that the two classes share characteristics, and that particular rat sample was more similar to that of the mouse.

Figure 5. Confusion matrix.

4.4 Accuracy per Epoch

Figure 6 shows the accuracy calculated per epoch. Accuracy refers to the percentage of classifications accurately spotted by the model during training.

Figure 6. Accuracy per Epoch.

4.5 Loss per Epoch

Based on the samples for the training model, the evaluation of how well a model has learned to predict correct classifications using these samples can be measured using Loss. The higher the confidence value for the accurate classification of the sample, the lower the loss value depicted in Figure 7.

Figure 7. Loss per Epoch.

4.6 Sample image recognition & categorization of the rodents

The sample image recognition and categorization of the rodents was done, and the data was collected in the form depicted in Figure 8.

Figure 8. Image recognition and categorization.

4.7 The Photovoltaic (PV) model

The proposed solar-powered motion sensor system ( Figure 9) operates autonomously by harnessing clean, renewable energy from the sun. The photovoltaic (PV) module is the cornerstone of this system, converting sunlight directly into electrical energy through the photovoltaic effect. A photovoltaic system is an array of PV modules that comprise a number of solar cells that generate electrical power.

Figure 9. Photovoltaic System.

4.8 Photon absorption

When sunlight, basically composed of photons, strikes the solar cells, the photons transfer their energy to electrons within the semiconductor material, hence exciting them (Vinod, Kumar, and Singh, 2018). This energy transfer is the initial step in converting solar energy into electrical energy. The photon absorption circuit is shown in Figure 10.

Figure 10. Photon absorption circuit.

4.9 Formation of electric potential in p-n junctions

Each solar cell contains a junction between two types of semiconductor materials: n-type and p-type. The junction between the n-type and p-type materials forms an electric field, causing the excited electrons to migrate to the p-type layer and leaving behind a static positive charge. Simultaneously, the holes wander across the junction, leaving behind a static negative charge. Eventually, a depletion zone forms at the junction, preventing further movement of charge carriers (Kirchartz and Rau, 2018).

The separated static positive and negative charges establish an electric field across the depletion zone. This field generates the voltage necessary to drive current through an external circuit. As the semiconductor continuously absorbs sunlight, the energy excites more electrons, causing them to jump to the conduction band and leave behind holes in the valence band (Chaudhery Mustansar Hussain, 2018). These freed electrons contribute to the electric current, moving toward the negative end, while holes move toward the positive end.

According to Chaudhery Mustansar Hussain (2018), when the absorbed photon energy exceeds the PV cell material’s bandgap energy, the atoms in the semiconductor collide, freeing more electrons and generating an electric current. This process ( Figure 11) effectively converts sunlight into electrical energy, harnessing solar power for practical use.

Figure 11. Formation of electric potential in p-n junctions.

4.10 Series and parallel connections

Individual solar cells generate a relatively small voltage, typically around 0.5 to 0.7 volts. To increase the voltage output, cells are wired in series within a module. Parallel connections of cell strings are used to achieve higher currents. This configuration allows a typical 60-cell PV module to produce approximately 30 volts DC at its maximum power point. (Malinowski, León and Haitham Abu-Rub, 2019).

4.11 Solar charge controller

A Solar Charge Controller performs several vital functions to optimize performance and safeguard equipment. Yassine and Anderson (2020) state that it prevents overcharging by regulating the voltage and current supplied to the batteries during sunlight hours, preserving battery life and preventing damage. Additionally, it blocks reverse current flow from batteries to panels, ensuring energy generated by the panels doesn’t drain back into the battery during low-light or nighttime conditions.