Paper 3: A Comprehensive Review on Tomato Leaf Disease Detection using Deep Learning Techniques
Authors : Pankaj Kumar Gupt, Dr. Anita Pal
Doi: https://doi.org/10.63920/tjths.43003
Abstract
Tomato cultivation is susceptible to various diseases, leading tocsignificant yield loss and economic impact. Rapid andcaccurate prediction is essential for timely intervention and mitigation. Deepclearning techniques, specifically CNN for the automated detection of tomato leaf diseases. The proposed methodology involves the acquisition of high-resolution images of tomato leaves, and training a CNN model to classify images into healthy or diseased categories. The dataset used for training and evaluation consists of labeled images encompassing early blight, late, along with healthy leaves. The CNN architecture is optimized through experimentation to achieve in terms of accuracy, precision, recall and F1-score. The trained model demonstrates promising results in accurately identifying various tomato leaf diseases, even in the presence of environmental variations and leaf deformities. Furthermore, the computational efficiency of the proposed approach allows for real-timecor near real-time disease detection, facilitating timely agricultural interventions. Overall, this research contributes to the advancement of automated agricultural monitoring systems, aiding farmers in early disease detection and management, thereby enhancing crop productivity and sustainability.
