Tomato Plant Disease Identification via Deep Learning Technique.

Tomatoes are edible berries that are utilized extensively around the world. Their scientific name is solanum lycopersicum. Many tomato varietals are primarily produced in temperate climates. Even though tomatoes are used as ingredients in food, they are botanically categorized as berries. Bacterial spot, Black mold, Gray spot, Late blight, and powdery mildew (PM) are the primary diseases that affect tomato plants. The most challenging process in this decade is also the early diagnosis of these disorders. In this study, the suggested method is applied to the assessment of disease severity and disease diagnosis in tomato plant images. There are various stages to the procedure. The tomato plant image is initially pre-processed using the Wavelet technique, and then the modified Watershed algorithm is used to segment the image using improved Double Sigmoid and Erosion (WSA-IDS&E). The segmented image is then processed in the feature extraction stage in order to extract features. The Gray Level Co-occurrence Matrix (GLCM), histogram, illness area, and pixel-based retrieved characteristics are fed into the CNN model, a deep learning method for identifying diseases. In addition, a novel training method, WUDHOA, which adjusts the CNN's weight, is introduced in this study to improve the performance of deep learning techniques. In addition, the severity of the tomato plant illness is measured in order to improve disease identification outcomes, where this severity assessment is taken into account using a fresh evaluation. Finally, the performance of the detection outcome is compared with other existing works to prove its efficiency. The conventional techniques have the lowest specificity ratings, such as AEO = 0. 8 4 7 3 , SSO = 0. 8 2 4 9 , SHO = 0. 8 6 7 2 , MFO = 0. 8 3 9 4 , BES = 0. 8 9 6 2 , WOA = 0. 8 7 4 9 , and DHO = 0. 8 8 4 6 , whereas the WUDHOA achieved a specificity of 0.9384. [ABSTRACT FROM AUTHOR]