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Title |
Early Detection of Rice Leaf Blast Disease using Deep-Learning Techniques |
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Author |
Syed Rehan Shah, Syed Muhammad Waqas Shah, Hadia Bibi, Mirza Murad Baig |
| Citation |
Vol. 24 No. 4 pp. 211-221 |
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Abstract |
Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches. |
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Keywords |
machine vision rice blast disease detection, image processing, transfer learning, deep neural networks, web-app |
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