Abstract
In India, Agriculture is an important sector to improve the economy. It provides over 70% employment overpopulation. So we have to solve their problem through computer-aided systems so that Farmers and Youngsters take an interest in Agriculture and work smartly and without tension. Traditional disease detection was based on feature selection such as color, texture, and shape; these features must be selected for classification, and accuracy was also not high.A Convolution Neural Network (CNN) based method has been proposed here along with Inception V3 for Tomato plant disease detection. It is done by transfer learning technology to retrain tomato disease dataset; an open-source platform is used for the same, which improved accuracy of tomato disease classification without the need of high-end configuration hardware. The accuracy percentage on training is 92.19%, and test accuracy is obtained as 93.03%
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Wadadare, S.S., Fadewar, H.S. (2022). Deep Learning Convolution Neural Network for Tomato Leaves Disease Detection by Inception. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_19
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