Taxonomic Classification and Sorting of Plant Leaf Diseases using DenseNet for Enhanced Detection and Categorization

Striking a balance between the predicted agricultural yield to meet the needs of an increasingly populous world is critical if food security on a global scale is to remain hopeful. As a result, a global food crisis may have begun because an agreeable balance was not achieved in this area. So, it's vital to stop anything, direct or indirect, from making this happen. To ensure that the world's food supply is adequate in both quality and quantity, it is crucial to cultivate plants in an environment free from harmful diseases. By applying deep learning to plant disease recognition, we can speed up the development of new technologies, make feature extraction more objective, and reduce the negative effects of artificially selecting disease spot features. A methodology for detecting leaf diseases using deep learning techniques is proposed in this research article. Ultimately, this research paper can help farmers and guarantee global food security by contributing to the development of efficient and effective methods for detecting and identifying leaf diseases.