Detection and Prevention of Tomato Leaf Disease using Convolutional Neural Network and Inception Net

A state-of-the-art technique for analyzing images that is still relatively new but yields consistent results is Deep Learning(DL). Many DL techniques are used for leaf disease categorization. Regardless of the cuisine, tomatoes are a staple in every kitchen and are one of the most popular vegetables. It ranks third in terms of widespread production, behind sweet potatoes and potatoes. The number and quality of tomato crops are, however, impacted by several illnesses. An approach to crop disease diagnosis by deep learning is used. For disease categorization and detection, a Convolutional Neural Network is employed. Two pooling and two Convolutional layers are employed inside the model. The suggested model beat pre-trained InceptionV3, and VGGI6, according to the experiment findings. 98% training accuracy and 88.17% testing accuracy were attained by the CNN model.