Image recognition is a critical part of machine vision. It identifies objects in images, such as specific types of tumor cells or leaves infected by a specific pathogen. Due to the complexity of solving such problems, deep learning (DL) is leveraged to achieve the required results. The challenge of DL is the requirement of a large number of training images before the learning task. ¹ Because training data is not always available at the beginning, and the new data arrives in the format of sequential stream. ² Furthermore, labeling a large number of images is labor-intensive. ³ To reduce the time consumption of laborious human labeling, it is ideal to incrementally label images to train a DL system and let the system label new images. ^{4 , 5} After human corrections, the new labels can enhance the system further until the required results are achieved. ^{6 , 7} We developed an open-source Python package named ROOSTER, to fit such a need. ROOSTER not only has a friendly graphic user interface to label images or their sub-images but also accepts any pre-trained models in the format of PyTorch for classification. We demonstrated the usage of ROOSTER in developing a machine vision system to detect wheat stripe rust using images from smartphones and drones.

We used ROOSTER to automatically label 200 images of plants containing no infection as the default status and 200 images of plants with all leaves infected as the alternative status (Stage 1 in Figure 2). These images are available in Underlying data. ¹¹ We used them for this initial training stage of RustNet, which was modified with a pre-trained ResNet-18. ⁹ The testing on a published independent set of images (5,818 diseased tiles and 14,542 non-diseased tiles) that were previously labeled manually (see Underlying data ¹⁰ ) suggested the two types of abundantly available images are beneficial to establishing essential predictability. The area under the receiver operating characteristic curve of true positive rate against false discovery rate is 0.23 compared to 0 for the random guess. Similarly, the area under the receiver operating characteristic curve of true positive rate against false positive rate is 0.64 compared to 0.5 for the random guess.

The labels of the images with all leaves infected in Stage 1 contained errors as some tiles do not contain leaves. We made the prediction on 20 new images ⁸ with all leaves infected, and manually corrected the prediction errors. The manual corrections with prediction navigation are much easier than labeling the raw images. The training (Stage 2) with the 20 new images dramatically boosted prediction accuracy (0.54 and 0.78 compared to 0.23 and 0.64 at Stage 1 for ROC of false discovery rate and false positive rate, respectively). Similarly, we made the prediction on 61 images with partial leaves infected, manually corrected the prediction errors, and added to the training data in Stage 2 to form Stage 3 training. The prediction accuracy was further improved (0.66 and 0.87 compared to 0.54 and 0.78 at Stage 2 for ROC of false discovery rate and false positive rate, respectively).

ROOSTER combines functions including automatic labeling, label prediction, and manual labeling in a user-friendly GUI (graphical user interface) to label and classifies images using any outsourced models in the format of PyTorch. The navigation of integrating prediction with labeling benefits human adjustment to iteratively improve predictability to use interactive deep learning to develop machine vision systems.