Deep Learning Approaches for Identifying and Classifying Plant Pathologies

Plant diseases are among the key problems confronting global agriculture, which leads to a decrease in crop yield and losses. Conventional detection procedures like human observation by experts are often time-consuming, subjective, and susceptible to making mistakes. Deep learning, that is, convolutional neural networks (CNNs), has turned out to be a great asset for plant disease detection automation with high accuracy. This paper provides an overview of state-of-the-art techniques emphasizing CNN-based models’ advantages over traditional machine learning algorithms like support vector machines and random forests. The method combines a hybrid deep model consisting of CNNs and transfer learning techniques for enhanced model performance and reliability. Powerful preprocessing techniques, including color normalization and augmentation, are incorporated to enhance classification accuracy across varied environmental conditions. The performance of the model is measured based on parameters such as accuracy, precision, recall, and F1-score. This research aims to assist in early intervention of disease by using a scalable and efficient solution and assist in sustainable agriculture. The findings identify the potential of deep learning in transforming plant disease control, reducing labor dependence, and promoting food security.