Paper Contents
Abstract
Monitoring plant health is essential in agriculture to increase crop yield and reduce disease-related losses. Conventional illness detection techniques are time-consuming, labor-intensive, and frequently require specialized knowledge. Plant health monitoring is essential in agriculture to maximize crop yield and reduce disease-related losses. In addition to being time-consuming and labor-intensive, traditional illness detection techniques frequently call for specialized knowledge. This study introduces a deep learning-based method for automatically identifying and categorizing plant leaf diseases using the Python TensorFlow and Keras frameworks. Using publicly accessible datasets that include pictures of both healthy and diseased plant leaves, a convolutional neural network (CNN) model is created and trained. The model successfully classifies a wide range of plant diseases, including leaf spot, rust, and powdery mildew, across many crop varieties. The system uses transfer learning, image augmentation, and data pretreatment methods to improve performance and lessen overfitting. The goal of this project is to use machine learning and artificial intelligence technology to help build smart farming solutions.
Copyright
Copyright © 2025 Yogeesh TR. This is an open access article distributed under the Creative Commons Attribution License.
