A Hybrid Deep Learning Approach for Plant leaf Disease Detection and Classification using YOLO and Transformer-based CNN

Abstract

Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts. This process is considered to be a tedious and time-consuming task due to the chances of human error during the process. To overcome the challenges of traditional methods of detecting and diagnosing diseases in plants, a deep learning-based system is proposed in this research study to detect and classify diseases in plants. This system uses the object detection model YOLOv8x and YOLOv10x to detect the objects in the images and classify the images accordingly. Deep learning models are used to classify the images of the plants. This study uses various deep learning models like the convolutional neural network model ResNet50 and EfficientNet, and the transformer model Vision Transformer and Swin Transformer. Moreover, a hybrid model is proposed in this study by combining the transformer and convolutional neural network model to improve the efficiency of the system in detecting and classifying the diseases of the plants. This system uses the PlantVillage dataset to classify the images of the plants and detect the diseases accordingly. From the results obtained in this study, it can be observed that the proposed system is highly efficient in detecting and classifying the diseases of the plants with the help of the transformer and hybrid model.