Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves.

CONVOLUTIONAL neural networks; TOBACCO mosaic virus; NOXIOUS weeds; EVIDENCE gaps; FEATURE extraction

A proper system for identifying leaf disease, which is crucial for developing agricultural areas, has been addressed in this research using a neural network approach. This study helps find plant illnesses and their stages whenever they occur. Fungal, bacterial, and viral infections are very harmful to plants. Five major tomato diseases have been classified in this research: bacterial spot, early blight, late blight, leaf mold, tomato mosaic virus, and healthy tomato plant leaves. The study underscores the importance of algorithmic adaptability to attain precision in leaf disease identification and emphasizes the potential for customized strategies in achieving accuracy. The classification is done by extracting color, shape, and texture features from a healthy tomato plant leaf image. The feature extraction method is carried out to proceed with the segmentation phase. Features extracted from segmented pictures are used as inputs to a classification algorithm. These five categories were used to finalize the illness categorization stage. The variety of five kinds of tomato leaf images yielded almost 99% classification accuracy. Furthermore, various research gaps have been identified to achieve a more open approach to detecting tomato diseases. [ABSTRACT FROM AUTHOR]

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