Diagnosis of fungi affected apple crop disease using improved ResNeXt deep learning model

Crop diseases are highly severe in agriculture which affect crop production. Apple is one of the most important fruit worldwide for economic and nutritional reasons. Even though much state-of-the-art has been done on crop disease detection and recognition, fungal disease-affected crops are still untouched. Fungal diseases can produce mycotoxins, which are toxic compounds that can contaminate food and feed, causing illness or death in humans and animals. This paper mainly focuses on detecting fungal diseases in apple crops. The improved ResNeXt, a deep learning model is proposed for the detection of apple crop diseases. The benchmark dataset has been used in the proposed model which contains 9395 images of diseases having 4 classes of apple. Initially, the transfer learning approach such as Inception-v7, ResNet on crops having fungal disease has been applied. Both approaches were not giving desired results. Further, an improved ResNeXt model variant of the Convolution neural network (CNN) for Fungal disease prediction has been proposed. Moreover, pre-processing step have been done for handling imbalanced dataset and also segmentation has been done to extract region of interest in crop. Comparison of the proposed model with the state-of-the-art approaches has been done. The proposed model attained an accuracy of 98.94%, recall of 99.2%, precision of 99.4%, and 99.2% of F1 score. The findings of this study can be utilized by the community for improving the crop production, as it demonstrates the potential for deep learning techniques to aid in the early detection of crop diseases, leading to improved crop yields and quality.