Plant Disease Prediction Using Image Processing

Abstract

Early detection of diseases in tomato plants is crucial for sustainable agriculture and food security. This paper presents a comprehensive plant disease detection system that uses image processing and machine learning techniques to identify diseases in tomato plants and recommend appropriate pesticides. The proposed system consists of multiple modules, including image preprocessing, feature extraction, disease classification, and pesticide recommendation. Experimental results show high accuracy in disease detection and demonstrate the system's potential to assist farmers in improving crop yield and minimizing losses.

Code and data availability

The paper describes tomato leaf image collection from 'publicly available datasets and field experiments' but names no dataset, provides no URL, DOI, or repository identifier, and contains no code, model, or data availability statement. No paper-specific public asset can be identified.

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