Deep Learning Based Plant Disease Detection and Treatment Guidance

Plant diseases are increasingly threatening world agricultural productivity and food security. The early detection and correct classification of these diseases are important in preventing loss of crops and ensuring sustainable crop production. In this paper, we present a deep learning method for plant disease detection based on the popular ResNet-50, which is a convolutional neural network (CNN) that have shortcut connections for enhancing feature extraction and classification performance. The Plant Village dataset, a well-known benchmark library of healthy and diseased plant leaf images, is used to train the model. With residual learning technique, the proposed model successfully deals with the issues of training deep networks like gradient vanishing, and achieves superior disease classification. In order to facilitate the access, a web service of FLDSS was implemented in Flask, in which users can upload images of plant leaves and obtain the immediate disease classification results. The experimental results show that this method can outperform traditional machine learning methods in terms of accuracy and efficiency, indicating the effectiveness of deep learning in agricultural disease diagnosis. This study provides a practical and scalable solution for real-time plant health monitoring for precision agriculture, ultimately assisting farmers and agribusiness professionals for disease management strategies.