Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN
Uniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting, as it affects subsequent field management and the crop yield. However, current transplanters are not able to meet this requirement due to the lack of accurate navigation systems. In this study, a pl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9165066/ |
| _version_ | 1818428202575462400 |
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| author | Shaomin Lin Yu Jiang Xueshen Chen Asim Biswas Shuai Li Zihao Yuan Hailin Wang Long Qi |
| author_facet | Shaomin Lin Yu Jiang Xueshen Chen Asim Biswas Shuai Li Zihao Yuan Hailin Wang Long Qi |
| author_sort | Shaomin Lin |
| collection | DOAJ |
| description | Uniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting, as it affects subsequent field management and the crop yield. However, current transplanters are not able to meet this requirement due to the lack of accurate navigation systems. In this study, a plant row detection algorithm was developed to serve as a navigation system of a rice transplanter. The algorithm was based on the convolutional neural network (CNN) to identify and locate rice seedlings from field images. The agglomerative hierarchical clustering (AHC) was used to group rice seedlings into seedling rows which were then used to determine the navigation parameters. The accuracies of the navigation parameters were evaluated using test images. Results showed that the CNN-based algorithm successfully detected rice seedlings from field images and generated a reference line which was used to determine navigation parameters (lateral distance and travel angle). Compared with mean absolute errors (MAE) test results, the CNN-based algorithm resulted in a deviation of 8.5 mm for the lateral distance and 0.50° for the travel angle, over the six intra-row seedling spacings tested. Relative to the test results, the CNN-based algorithm had 62% lower error for the lateral distance and 57% lower error for the travel angle when compared to a classical algorithm. These results demonstrated that the proposed algorithm had reasonably good accuracy and can be used for the rice transplanter navigation in real-time. |
| first_indexed | 2024-12-14T14:57:53Z |
| format | Article |
| id | doaj.art-89f728cc99334820ba1df443516f660f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:57:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-89f728cc99334820ba1df443516f660f2022-12-21T22:56:56ZengIEEEIEEE Access2169-35362020-01-01814723114724010.1109/ACCESS.2020.30158919165066Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNNShaomin Lin0Yu Jiang1Xueshen Chen2Asim Biswas3https://orcid.org/0000-0003-0801-3546Shuai Li4Zihao Yuan5Hailin Wang6Long Qi7https://orcid.org/0000-0002-7987-8929Department of Agricultural Engineering, College of Engineering, South China Agricultural University, Guangzhou, ChinaModern Educational Technology Center, South China Agricultural University, Guangzhou, ChinaDepartment of Agricultural Engineering, College of Engineering, South China Agricultural University, Guangzhou, ChinaSchool of Environmental Sciences, University of Guelph, Guelph, ON, CanadaDepartment of Agricultural Engineering, College of Engineering, South China Agricultural University, Guangzhou, ChinaDepartment of Agricultural Engineering, College of Engineering, South China Agricultural University, Guangzhou, ChinaCollege of Electronic Engineering, South China Agricultural University, Guangzhou, ChinaDepartment of Agricultural Engineering, College of Engineering, South China Agricultural University, Guangzhou, ChinaUniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting, as it affects subsequent field management and the crop yield. However, current transplanters are not able to meet this requirement due to the lack of accurate navigation systems. In this study, a plant row detection algorithm was developed to serve as a navigation system of a rice transplanter. The algorithm was based on the convolutional neural network (CNN) to identify and locate rice seedlings from field images. The agglomerative hierarchical clustering (AHC) was used to group rice seedlings into seedling rows which were then used to determine the navigation parameters. The accuracies of the navigation parameters were evaluated using test images. Results showed that the CNN-based algorithm successfully detected rice seedlings from field images and generated a reference line which was used to determine navigation parameters (lateral distance and travel angle). Compared with mean absolute errors (MAE) test results, the CNN-based algorithm resulted in a deviation of 8.5 mm for the lateral distance and 0.50° for the travel angle, over the six intra-row seedling spacings tested. Relative to the test results, the CNN-based algorithm had 62% lower error for the lateral distance and 57% lower error for the travel angle when compared to a classical algorithm. These results demonstrated that the proposed algorithm had reasonably good accuracy and can be used for the rice transplanter navigation in real-time.https://ieeexplore.ieee.org/document/9165066/Agglomerative hierarchical clusteringconvolutional neural networkimagenavigationriceseedling |
| spellingShingle | Shaomin Lin Yu Jiang Xueshen Chen Asim Biswas Shuai Li Zihao Yuan Hailin Wang Long Qi Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN IEEE Access Agglomerative hierarchical clustering convolutional neural network image navigation rice seedling |
| title | Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN |
| title_full | Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN |
| title_fullStr | Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN |
| title_full_unstemmed | Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN |
| title_short | Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN |
| title_sort | automatic detection of plant rows for a transplanter in paddy field using faster r cnn |
| topic | Agglomerative hierarchical clustering convolutional neural network image navigation rice seedling |
| url | https://ieeexplore.ieee.org/document/9165066/ |
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