DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes
Rice stands as a crucial staple food globally, with its enduring sustainability hinging on the prompt detection of rice leaf diseases. Hence, efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical te...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-03-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721723000430 |
| _version_ | 1797262862874312704 |
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| author | P. Isaac Ritharson Kumudha Raimond X. Anitha Mary Jennifer Eunice Robert Andrew J |
| author_facet | P. Isaac Ritharson Kumudha Raimond X. Anitha Mary Jennifer Eunice Robert Andrew J |
| author_sort | P. Isaac Ritharson |
| collection | DOAJ |
| description | Rice stands as a crucial staple food globally, with its enduring sustainability hinging on the prompt detection of rice leaf diseases. Hence, efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical testing. In the recent past, the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment, which proves to be time-consuming and inefficient. This study offers a remedy by harnessing Deep Learning (DL) and transfer learning techniques to accurately identify and classify rice leaf diseases. A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets, categorized into 9 classes irrespective of the extent of disease spread across the leaves. These classes encompass diverse states including healthy leaves, mild and severe blight, mild and severe tungro, mild and severe blast, as well as mild and severe brown spot. Following meticulous manual labelling and dataset segmentation, which was validated by horticulture experts, data augmentation strategies were implemented to amplify the number of images. The datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks models. Their performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, and Inception V3. The effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images, yielding an exceptional accuracy of 99.94%, surpassing the benchmarks set by existing state-of-the-art models. Further, the layer wise feature extraction is also visualized as the interpretable AI. |
| first_indexed | 2024-03-08T22:45:22Z |
| format | Article |
| id | doaj.art-8b580ff0888f4af5859018f6871671ec |
| institution | Directory Open Access Journal |
| issn | 2589-7217 |
| language | English |
| last_indexed | 2024-04-25T00:03:52Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj.art-8b580ff0888f4af5859018f6871671ec2024-03-14T06:15:52ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172024-03-01113449DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypesP. Isaac Ritharson0Kumudha Raimond1X. Anitha Mary2Jennifer Eunice Robert3Andrew J4Department of CSE Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, IndiaDepartment of CSE Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, IndiaDepartment of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, IndiaDepartment of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India; Corresponding authors.Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India; Corresponding authors.Rice stands as a crucial staple food globally, with its enduring sustainability hinging on the prompt detection of rice leaf diseases. Hence, efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical testing. In the recent past, the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment, which proves to be time-consuming and inefficient. This study offers a remedy by harnessing Deep Learning (DL) and transfer learning techniques to accurately identify and classify rice leaf diseases. A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets, categorized into 9 classes irrespective of the extent of disease spread across the leaves. These classes encompass diverse states including healthy leaves, mild and severe blight, mild and severe tungro, mild and severe blast, as well as mild and severe brown spot. Following meticulous manual labelling and dataset segmentation, which was validated by horticulture experts, data augmentation strategies were implemented to amplify the number of images. The datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks models. Their performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, and Inception V3. The effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images, yielding an exceptional accuracy of 99.94%, surpassing the benchmarks set by existing state-of-the-art models. Further, the layer wise feature extraction is also visualized as the interpretable AI.http://www.sciencedirect.com/science/article/pii/S2589721723000430Rice leaf diseaseDeep learningCNNCrop yieldAgricultureFood security |
| spellingShingle | P. Isaac Ritharson Kumudha Raimond X. Anitha Mary Jennifer Eunice Robert Andrew J DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes Artificial Intelligence in Agriculture Rice leaf disease Deep learning CNN Crop yield Agriculture Food security |
| title | DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes |
| title_full | DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes |
| title_fullStr | DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes |
| title_full_unstemmed | DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes |
| title_short | DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes |
| title_sort | deeprice a deep learning and deep feature based classification of rice leaf disease subtypes |
| topic | Rice leaf disease Deep learning CNN Crop yield Agriculture Food security |
| url | http://www.sciencedirect.com/science/article/pii/S2589721723000430 |
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