Deep Learning Based Classification and Prediction on Vegetable Crop Leaf

Globally speaking, India is a country that is growing quickly. Crop diseases pose a severe danger to food security, yet they are still hard to identify. Due to their reliance on a manually created feature extraction process, these systems' accuracy has reached its peak. This approach classification method needs to incorporate CNN to surpass grading accuracy for tomato leaf pestilence. To properly describe and categorize tomato infections, the Deep Learning algorithm is used. Using a sample of 3000 frames of tomato leaves with nine various pestilences and a better and healthier leaf, the full simulation was carried out using Google Colab. The targeted area of the input photographs is first segregated from the genuine snaps after preprocessing the input images. Second, the frames are further processed using various CNN model hyper-parameters. CNN also extracts additional qualities from frames, such as colours, borders, and textures. The results reveal that the predictions made by the demonstrated replica are 98.49% precise.