Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application

Abstract

The "Leaf Disease Detection" system addresses the critical challenge of plant diseases in agriculture through the implementation of an automated solution leveraging deep learning techniques. In this comprehensive endeavor, convolutional neural networks (CNNs), specifically DenseNet-121, ResNet-50, VGG-16, and Inception V4, are fine-tuned for efficient and accurate identification of plant diseases. The project utilizes the Plant Village dataset, encompassing 54,305 images across 38 plant disease classes, to conduct a comparative analysis of model performance. DenseNet-121 emerged as the top-performing model, achieving an exceptional 99.81% classification accuracy, surpassing other state-of-the-art models. The system's methodology strategically employs transfer learning to overcome computational challenges associated with training deep CNN layers. This approach, coupled with the multi-class classification strategy, proves robust in handling diverse plant species and diseases within each class. The results highlight the superior efficiency of transfer learning in comparison to building models from scratch, showcasing the potential for real-world applications in agriculture. The system's success is attributed to the careful optimization of hyper parameters and the adoption of advanced deep learning techniques, offering a promising avenue for automated and accurate plant disease detection, with implications for improving agricultural practices, minimizing economic losses, and ensuring global food security.