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Browsing BS by Author "Amna Ilyas"

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    Early detection of potato leaf disease using convolutional neural network.
    (UMT Lahore, 2024-07-31) Taha Shabbir; Amna Ilyas; Afifa Islam; Zainab Irshad.
    Agriculture often is the only source of income in several countries with the losses of crops due to diseases. These ailments make the whole yield be obliterated by going after the harvests at various stages. It, therefore, dictates that farmers should be in a position to identify the particular disease that has attacked their crops to "save the crops" and thus increase their production. In any case, since there are so many sorts of ailments, it may be very provoking for ranchers to distinguish which kind has contaminated their yield. Here, in the project, we can study about the diseases that are going to affect the leaf. Potato development encounters serious obstructions with leaf diseases, influencing result and quality. Traditional detection methods are often imprecise and require a lot of human effort. The following research thus proposes an exciting approach for the early identification of potato leaf diseases that makes use of convolutional neural networks. The main objective is to create a reliable cnn model in order to classify diseases using images of potato-leaf diseases, which are trained by a dataset. The main goal is to create a reliable cnn model that can accurately classify diseases using image of potato leaf diseases that have been trained on a dataset. Further, this involves a user-friendly web application providing an easy interface for farmers and agricultural experts to submit photos of leaves in exchange for fast diagnosis results. The cnn architecture consists of convolutional layers for feature extraction and dense layers for classification. All of these layers are finely tuned through a rigorous training process. Model evaluation results manifest how well this model recognizes the different diseases potatoes have on leaves.