Abstract: Agriculture is a key industry for world food security, but crop diseases are a major threat to agriculture productivity. Early and precise detection of diseases is critical to avoid loss of yield and achieve a sustainable agricultural system. Most of the farmers, particularly in India, have a hard time with disease diagnosis because they lack proper infrastructure, which results in improper management of the crops and lower yields. Machine intelligence and advanced learning provide cutting-edge techniques for detecting diseases early through computer vision approaches. In our research, we created TomatoShield, an app based on a mobile platform that has the capability of disease identification. We tested CNN models like Xception for classifying tomato plant disease using a 22,200 images dataset of leaves from Kaggle. The Xception model had achieved the accuracy of 93.64%. The app, developed with Python's Kivy library, allows farmers to take or upload images of leaves for immediate diagnosis. It also includes storing results in an SQLite database for easier recovery and analysis, delivering actionable information to farmers to increase agricultural productivity and crop health.
Keywords: Plant Disease Prediction, Xception, CNN, Disease Treatment, Image Classification, Machine Learning, Deep Learning.
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DOI:
10.17148/IJARCCE.2025.14404
