Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach
Smitha Padshetty, Ambika
Abstract
Plant diseases can result in significant yield losses, posing a threat to food security and economic stability. Deep neural networks, particularly Convolutional Neural Networks (CNNs), have shown exceptional success in image classification tasks, often surpassing human-level performance. However, conventional methods for leaf disease detection relied on manual inspection by agricultural experts, leading to limited scalability and precision. To tackle these challenges, this research introduces a novel approach called the Leaky Rectilinear Residual Network (LRRN) for plant leaf disease detection. The LRRN model comprises three key modules—data pre-processing, feature extraction, and classification. It integrates ResNet architecture with the Leaky ReLU activation function to classify plant diseases. Experimental evaluations were performed on affected plant leaf disease images from the Plant Village dataset, utilizing performance evaluation metrics to assess the proposed model. The achieved results were compared to state-of-the-art techniques, demonstrating superior accuracy (94.56%), precision (93.48%), F1-scores (92.83%), recall (93.12%), and specificity (92.58%). These findings substantiate the effectiveness of the proposed LRRN method of plant leaf disease detection.