Abstract
In our society, one of the major problems in the agriculture field is plant diseases. Most of the farmers are unaware of such diseases. So, the detection of various diseases of plants is very essential to prevent damages. This research work aimed to classify and detect the plant’s diseases mechanically especially for the tomato plant. Classification is mainly done by feature extraction and convolution neural network. In fuzzy logic algorithm using segmentation for tomatoes and its leaves. Here, Python programming language, OPENCV library is used to manipulate raw input image and to train on CNN architecture and creating a model that can predict the type of diseases. At the final result, the minority of infections commonly formed in tomato flora. Hence, three different infections namely late blight, gray spot and bacterial canker are identified.
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Vijayalakshmi, L., Sornam, M. (2022). Tomato Disease Detection Using Convolutional Neural Network and Fuzzy Logic. In: Chandramohan, S., Venkatesh, B., Sekhar Dash, S., Das, S., Sharmeela, C. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1361. Springer, Singapore. https://doi.org/10.1007/978-981-16-2674-6_28
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DOI: https://doi.org/10.1007/978-981-16-2674-6_28
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