Abstract
In India, Tomato is broadly yielded crop. There are many diseases which affects the production of Tomato crop. We need better and perfect guidance for the correct prediction of leaf diseases and to differentiate between the similar diseases in visuals. Using deep learning, we can decrease the tediousness in the disease detection. This paper analyze and compare the convolution neural network models like VGG architecture (built from scratch), VGG architecture (using pre-trained weights), GoogLeNet, AlexNet to detect the of tomato plant diseases using leaves images. Resizing and conversion of input dataset is done in image pre-processing. The open access dataset of images containing 6 different disease classes and a healthy class is used for the model training.
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Kathole, V., Munot, M. (2022). Deep Learning Models for Tomato Plant Disease Detection. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_52
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DOI: https://doi.org/10.1007/978-981-19-0840-8_52
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