Deep Convolutional Neural Networks for Plant Disease Detection: A Mobile Application Approach (Agri Bot)

Abstract

Plant diseases imperil global food security, decimating crop yields and endangering farmers’ livelihoods. Rapid, accurate detection remains a challenge, particularly in resource-constrained environments lacking portable tools. Our contribution, Agri Bot, introduces a pioneering deep convolutional neural network (CNN) model, uniquely optimized for mobile deployment, transforming plant disease diagnosis. This novel model integrates a lightweight architecture with advanced feature extraction, achieving an exceptional 97.30% accuracy and 98.76% area under the curve (AUC). Unlike computationally intensive traditional CNNs, Agri Bot’s innovative design—featuring a hybrid convolutional autoencoder, max pooling, and dropout layers—ensures high-speed, real-time performance on mobile devices. Comparative studies reveal Agri Bot’s superiority, surpassing state-of-the-art models like VGG16 (71.48%) and ResNet50 (96.46%), while rivaling InceptionV3 (99.07%) with significantly lower computational demands. By delivering precise, accessible diagnostics to remote regions, Agri Bot revolutionizes agricultural disease management, enhancing crop resilience and global food security.