SYSTEM FOR DETECTING TOMATO PLANT DISEASES USING IMAGE PROCESSING

computer Engineering

Plant infections are one of the biggest issues in the agricultural business. Viral, bacterial, fungal, and other causes can all contribute to plant diseases. The majority of farmers are ignorant of these illnesses. Since these illnesses can harm not only the plants themselves but also the farmers and the entire agricultural ecology, it is crucial to identify them as soon as possible. Regarding these practical concerns, the goal of this research was to automatically classify and identify plant diseases, namely those that affect tomato plants. Raspberry Pi is the primary computing device according to the hardware requirements. The main project process is image processing, which comprises feature extraction, picture ROI adjustment, feature acquisition, and CNN-based classification. Here, the raw input image is modified using the Python programming language and the OPENCV module. Image data is gathered from the trusted web source in order to train the CNN architecture and develop a machine learning model that can identify the types of diseases. As a result, a small number of diseases that typically affect tomato plants are discovered, including Late blight (training 100, test 22), Gray spot (training 96, test 19), and Bacterial Canker (training 90, test 23).

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