Abstract
Plant disease is the key issue for the farmers, which leads to lesser income and minimal outcome. Pest affected crop also results in small agricultural production of the country. The traditional way of detecting and recognizing plant diseases with the bare eyes by farmers and experts is time consuming, expensive and erroneous. Hence, in this chapter, we use deep convolutional networks algorithms for leaf image classification to provide accurate results. Hence, CNN model is used for distinguishing the healthy and diseased nodes of the crop. The developed model can identify seven types of plant diseases present in the leaf along with the healthy leaves. The dataset used in the study is collected from the controlled environment consisting of 8,685 images of leaves. These images are utilized as an input for training and validating the CNN model demonstrating better performance of it in classifying the plant diseases.
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Rishiwal, V., Chaudhry, R., Yadav, M., Singh, K.R., Yadav, P. (2023). Artificial Intelligence Based Plant Disease Detection. In: Rishiwal, V., Kumar, P., Tomar, A., Malarvizhi Kumar, P. (eds) Towards the Integration of IoT, Cloud and Big Data. Studies in Big Data, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-99-6034-7_5
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