Classification and Validation of Tomato Leaf Disease Using Deep Learning Techniques
DOI:
https://doi.org/10.63503/j.ijaimd.2024.9Keywords:
Tomato leaf disease, VGG19, InceptionV3, Colorectal, lycopeneAbstract
Tomatoes are regarded as fruits since they fit the botanical definition of a fruit because they are the fleshy parts of a plant that enclose its seeds. There are approximately 10 different kinds of diseases for a tomato plant, which is huge in number and can create huge losses for the farmers. This paper focuses on the classification of tomato plant leaf diseases using Convolution Neural Net-work (CNN) a deep learning technique that is especially employed for image recognition and pixel data processing activities. CNN has been used to identify whether the given photo of the plant is of a healthy or unhealthy part. The secondary dataset that was taken was trained using two algorithms Incep-tion-v3 and VGG19. VGG19 is a 19 layered algorithm, comprising 16 convolu-tional layers and 3 fully linked layers. Inception-v3 is the algorithm is a CNN-based algorithm that has 48 layers in it. The pre-trained networks can categorize photos into several different item categories, including several an-imals, types, diseases and much more. The dataset that is being used to train and validate the model is secondary data taken from Kaggle. The models were trained with 1900 images of healthy tomato plants, and 1800 images of un-healthy tomato plants. The model was also validated with 500 images of healthy and 1080 images of unhealthy tomato plants. The Inception V3 model was able to achieve an accuracy of 98% and a validation accuracy of 89% and VGG19 achieved an accuracy of 94%. Inception-v3 was the chosen model for the paper.
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