Abstract
Despite being reliant on agriculture for the provision of food, many nations, including Bangladesh, struggle to feed their populations enough. The potato (Solanum tuberosum) is Bangladesh’s second-most popular and in-demand crop. But deadly diseases late blight and early blight cause an enormous loss in potato production. To increase plant yields, it is important to identify the symptoms of these diseases in plants in the early stage and advise farmers on how to respond. This project involves the development of a web application that allows users to upload images of potato leaves and then use a trained CNN model to diagnose the disease from those images. After comparing with different Convolutional Neural Network Models (EfficientNetB0-B3, MobileNetV2, DenseNet121, and ResNet50V2), MobileNetV2 was able to reach an accuracy of 96.14% with the test dataset in detecting early blight and late blight. So, MobileNetV2 is deployed in the web application to detect the disease of the input image. The developed web application offers a user-friendly interface that enables farmers who are less tech-savvy to use this method to identify disease at an early stage and prevent it.
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Hasi, J.M., Rahman, M.O. (2023). Potato Disease Detection Using Convolutional Neural Network: A Web Based Solution. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_4
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