The spread of plant diseases is influenced by a variety of environmental and pathogen factors that not only lower the production of fruits and grains but also cause quality deterioration. The timely detection and evaluation of diseases in food plants are crucial for the health of the agricultural industry and the country’s capacity to produce enough food. This research focused on prevalent illnesses in wheat and pea plants to explore the creation of an autonomous disease detection system designed for the unique traits of the examined crops, which can be adapted to the particularities of other crops. The experiments with artificial intelligence (AI) models included both traditional machine learning and sophisticated deep learning models, including customized models and transfer learning models. In this study, our framework employed the capabilities of Transformers, Random Forest, VGG16, a custom convolutional neural network (CNN) model, and two versions of You Only Look Once (YOLO), including both v5 and v8. The employability of classical machine learning techniques aims to gauge the computational feasibility of advanced techniques such as transformers and YOLO. This work also focused on the development of a standard dataset, which was developed by collecting healthy and diseased samples from various crop fields in Pakistan. The obtained results demonstrate the improved performance of the fine-tuned VGG16 model, transformers, and the customized version of CNN as compared to the Random Forest model and YOLOv8 algorithms, in terms of disease detection and classification accuracy. Moreover, the usability of YOLO versions and detection transformers for real-time disease identification has shown promising results, which is evident in its prospects for plant disease monitoring systems.
Iqbal et al. (Mon,) studied this question.