A robust deep learning approach for tomato plant leaf disease localization and classification
Abstract Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however,...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21498-5 |
| _version_ | 1797984664090050560 |
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| author | Marriam Nawaz Tahira Nazir Ali Javed Momina Masood Junaid Rashid Jungeun Kim Amir Hussain |
| author_facet | Marriam Nawaz Tahira Nazir Ali Javed Momina Masood Junaid Rashid Jungeun Kim Amir Hussain |
| author_sort | Marriam Nawaz |
| collection | DOAJ |
| description | Abstract Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well. |
| first_indexed | 2024-04-11T07:06:40Z |
| format | Article |
| id | doaj.art-33da5247d5c74a379f7b0c14d393a172 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T07:06:40Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-33da5247d5c74a379f7b0c14d393a1722022-12-22T04:38:23ZengNature PortfolioScientific Reports2045-23222022-11-0112111810.1038/s41598-022-21498-5A robust deep learning approach for tomato plant leaf disease localization and classificationMarriam Nawaz0Tahira Nazir1Ali Javed2Momina Masood3Junaid Rashid4Jungeun Kim5Amir Hussain6Department of Computer Science, University of Engineering and Technology TaxilaFaculty of Computing, Riphah International UniversityDepartment of Software Engineering, University of Engineering and Technology TaxilaDepartment of Computer Science, University of Engineering and Technology TaxilaDepartment of Computer Science and Engineering, Kongju National UniversityDepartment of Computer Science and Engineering, Kongju National UniversityCentre of AI and Data Science, Edinburgh Napier UniversityAbstract Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.https://doi.org/10.1038/s41598-022-21498-5 |
| spellingShingle | Marriam Nawaz Tahira Nazir Ali Javed Momina Masood Junaid Rashid Jungeun Kim Amir Hussain A robust deep learning approach for tomato plant leaf disease localization and classification Scientific Reports |
| title | A robust deep learning approach for tomato plant leaf disease localization and classification |
| title_full | A robust deep learning approach for tomato plant leaf disease localization and classification |
| title_fullStr | A robust deep learning approach for tomato plant leaf disease localization and classification |
| title_full_unstemmed | A robust deep learning approach for tomato plant leaf disease localization and classification |
| title_short | A robust deep learning approach for tomato plant leaf disease localization and classification |
| title_sort | robust deep learning approach for tomato plant leaf disease localization and classification |
| url | https://doi.org/10.1038/s41598-022-21498-5 |
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