Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone
The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of ric...
| المؤلفون الرئيسيون: | , , , , , , , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
MDPI AG
2023-08-01
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| سلاسل: | Agronomy |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2073-4395/13/8/2139 |
| _version_ | 1827730699155668992 |
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| author | Jizhong Deng Chang Yang Kanghua Huang Luocheng Lei Jiahang Ye Wen Zeng Jianling Zhang Yubin Lan Yali Zhang |
| author_facet | Jizhong Deng Chang Yang Kanghua Huang Luocheng Lei Jiahang Ye Wen Zeng Jianling Zhang Yubin Lan Yali Zhang |
| author_sort | Jizhong Deng |
| collection | DOAJ |
| description | The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses. |
| first_indexed | 2024-03-11T00:11:25Z |
| format | Article |
| id | doaj.art-517d32ef5e674b9db869588169c0e42b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T00:11:25Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-517d32ef5e674b9db869588169c0e42b2023-11-18T23:55:33ZengMDPI AGAgronomy2073-43952023-08-01138213910.3390/agronomy13082139Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile PhoneJizhong Deng0Chang Yang1Kanghua Huang2Luocheng Lei3Jiahang Ye4Wen Zeng5Jianling Zhang6Yubin Lan7Yali Zhang8College of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaThe realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses.https://www.mdpi.com/2073-4395/13/8/2139rice diseases and insect pestsmobile phone applicationdeep learningobject detection |
| spellingShingle | Jizhong Deng Chang Yang Kanghua Huang Luocheng Lei Jiahang Ye Wen Zeng Jianling Zhang Yubin Lan Yali Zhang Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone Agronomy rice diseases and insect pests mobile phone application deep learning object detection |
| title | Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone |
| title_full | Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone |
| title_fullStr | Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone |
| title_full_unstemmed | Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone |
| title_short | Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone |
| title_sort | deep learning based rice disease and insect pest detection on a mobile phone |
| topic | rice diseases and insect pests mobile phone application deep learning object detection |
| url | https://www.mdpi.com/2073-4395/13/8/2139 |
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