Tomato and Potato Plant Disease Prediction Using Deep Learning and Edge AI

Praful R Arkachari; Varun K S

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Authors

Praful R Arkachari Student, Department of MCA, GM University
Author
Varun K S Assistant professor, GM University
Author

DOI:

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Keywords:

Plant Disease Detection, Edge AI, Convolutional Neural Networks (CNN), Mobile Application, TensorFlow Lite, Transfer Learning, Tomato Leaf Disease, Potato Leaf Disease, Image Classification, Deep Learning, Smart Agriculture, Computer Vision, Real-Time Dia

Abstract

Plant diseases significantly impact global crop yield and food security. Traditional manual disease identification is slow, subjective, and often inaccurate. This paper proposes an AI-based mobile system for real-time detection of tomato and potato leaf diseases using Convolutional Neural Networks (CNNs) and Edge AI. The model is trained on a Kaggle dataset and deployed on mobile devices using TensorFlow Lite, enabling offline inference. The system provides fast, accurate, and user-friendly disease diagnosis for farmers in remote regions. The framework demonstrates high potential for modernizing crop protection and supporting sustainable agriculture.