Plant Disease Detection in Tomato Plants Using  Deep Learning Technique

Author(s)	Mr. Nitesh Singh, Mr. Ankur Tiwari, Mr. Sumit Upadhyay, Dr. Pushpa Choudhary
Country	India
Abstract	Plant diseases in tomato crops pose significant challenges to agricultural productivity, often leading to reduced yield and quality. This study presents a machine learning-based system for early disease detection in tomato plants using convolutional neural networks (CNNs). A dataset of 10,000 images was used, encompassing 10 classes of healthy and diseased tomato leaves. Our CNN-based model achieved a detection accuracy of 95.6%, demonstrating its effectiveness in identifying diseases like late blight and leaf mold. This work provides a scalable and cost-effective approach to improve crop management. Additionally, we propose practical deployment methods for real-world applications.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-16
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.44495
Short DOI	https://doi.org/g9kfnd

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