Corn leaf disease detection system using Convolutional Neural Network / Wan Nurul Izzah Abd Hadi, Iman Hazwam Abdul Halim
Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as kno...
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| Main Authors: | , |
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| Format: | Book Section |
| Language: | English |
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College of Computing, Informatics and Media, UiTM Perlis
2023
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/100635/1/100635.pdf https://ir.uitm.edu.my/id/eprint/100635/ |
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| Summary: | Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as knowledge in plant diseases to achieve. Image processing is thus used in the process of detecting plant diseases. This project mainly focuses on corn leaves disease detection using convolutional neural network. The Xception model, which is a part of a convolutional neural network capable of classifying images into broad object categories, would be the model of choice for this image classification. Using Convolutional Neural Network (CNN), this study aims to build and test a web-based image classification tool for identifying corn leaf diseases detection. This research dataset is trained by analyzing a big dataset that contains pictures of various diseases that might affect corn leaves as well as pictures of corn leaves that are healthy in order to precisely identify them. The data were then analysed using a methodology known as the Agile model, which included phases for planning, requirement analysis, design, development, testing, and documentation. The findings from the study provide evidence on the precision with which the Xception model performed has reached 92.11 percent when applied to the datasets that have been gathered. Strongly, the results of the study will emphasize the need for developing a thorough image classification system in detecting plant diseases without human intervention. |
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