Detecting Crop Pathogens with Convolutional Neural Networks

Crop diseases caused by pathogens pose a significant threat to global food security, leading to considerable agricultural losses and economic consequences. Detecting crop diseases early and precisely enables proper control measures and response strategies. Traditional disease identification techniques are time consuming, expensive and reliant on expert knowledge which makes them not practical on large scale farming. To address these challenges, this is an application of Convolutional Neural Networks (CNNs) for computer vision-based automated detection of crop pathogens. A model was developed and trained on a dataset multilayer CNN model data collected from real world sources and legacy databases. The approach takes advantage of recent feature extraction methods to distinguish and classify fungal and bacterial diseases of nearly 10 plants like potato, tomato, apple, corn, etc. with 38 different class of plant diseaes with accuracy levels of 94.87%. In addition, principal preprocessing techniques used included image augmentation, normalization and resizing that enhanced the vigor and generalization capability of the model. Hyperparameters were tested to minimize overfitting as well as to enhance performance on the unseen data. A web- based application was also developed for practical use, allowing farmers to upload images of diseased plants and receive quick diagnoses. This system is highly adaptable, allowing deployment on mobile devices and web platforms, making it accessible in both technologically advanced and remote regions. Thus improvement in crop disease detection is obtained by utilizing the combination of a MySQL database and a Django-based backend to provide real-time access to information on disease descriptions, sources, and treatment suggestions, thereby bridging the gap between detection and response, and thus improving accessibility and decision-making capacity for farmers. With this system, crop losses and agricultural losses could be reduced dramatically and agricultural cycles may improve significantly with early disease detection and appropriate intervention. productivity, contributing to global food security. This work proves the efficacy of CNN-based approaches in solving key challenges in precision agriculture.