Deep Learning–Driven Identification and Classification of Plant Leaf Diseases Using Convolutional Neural Networks
Keywords:
Plant leaf disease detection · Convolutional Neural Networks (CNN) · ResNet · VGG · Efficient Net · Deep learning · Transfer learning · Agricultural AIAbstract
Plant diseases significantly impact global agricultural productivity and food security. Traditional identification methods rely on manual observation by experts, which is time-consuming, subjective, and often impractical at large scales. This study investigates the application of state-of-the-art deep learning techniques — specifically Convolutional Neural Networks (CNNs) including ResNet, VGG, and EfficientNet — to automate the process of plant leaf disease identification and classification. We systematically compare model performance on benchmark datasets using metrics such as accuracy, precision, recall, and F1-score. Our experimental results indicate that transfer-learning-based CNNs — particularly EfficientNet variants — achieve superior classification performance while managing computational efficiency. Results also reveal that data preprocessing and augmentation significantly enhance model robustness. The findings suggest that deep learning–based methods can provide reliable and scalable tools for early disease diagnosis in precision agriculture.
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