Original Article ISSN (Online): 2454-1907 International Journal of Engineering Technologies and Management Research June 2022 9(6), 1–14 How to cite this article (APA): Saxena, N., and Sharma, N. (2022). Tomato Leaf Disease Prediction Using Transfer Learning. International Journal of Engineering Technologies and Management Research, 9(6), 1-14. doi: 10.29121/ijetmr.v9.i6.2022.1177 1 TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING Niharika Saxena 1 , Dr. Neha Sharma 2 1 M.E. Scholar, Electronics and Communication, Ujjain Engineering College, India 2 Professor, Electronics and Communication, Ujjain Engineering College, India ABSTRACT In India's agricultural lands, tomatoes are the most widely planted vegetable crop. In spite it can grow in warmer climates, certain climatic conditions and other factors may contribute to the growth of the tomato plant. In addition to these natural and man-made disasters, crop disease is a serious problem in agricultural production leading to economic losses. Therefore, early detection of disease can provide better results than current diagnostic algorithms. As a consequence, in-depth computer-based learning methods may be used to diagnose diseases early. This study carefully examines the disease classification and diagnostic techniques used to diagnose tomato leaf disease. The advantages and disadvantages of the methods provided are also discussed in this study. Eventually, using hybrid deep- learning architecture, this study provides a way to diagnose diseases early to diagnose tomato leaf disease. Received 20 April 2022 Accepted 25 May 2022 Published 21 June 2022 Corresponding Author Niharika Saxena, niharika31saxena@gmail.com DOI 10.29121/ijetmr.v9.i6.2022.1177 Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Copyright: © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. With the license CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author. Keywords: Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Leaf Disease, Crop Disease, Tomato Leaf 1. INTRODUCTION Precision farming is the next step in the evolution of agriculture. Precision agriculture may boost agricultural output by combining science and technology. Precision farming also entails reducing pesticides and illnesses by accurately calculating the number of pesticides needed. Precision farming has improved several agriculture sectors as it transitions from conventional ways to new approaches. Precision farming's sole goal is to collect real-time data to increase agricultural yield and crop quality. Agriculture is much more than just a means of feeding the world's growing population. Plant diseases have also had a major impact on agricultural and forestry businesses. As a consequence, early detection and