期刊
IEEE ACCESS
卷 8, 期 -, 页码 147231-147240出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3015891
关键词
Navigation; Agriculture; Green products; Training; Proposals; Clustering algorithms; Cameras; Agglomerative hierarchical clustering; convolutional neural network; image; navigation; rice; seedling; transplanting
资金
- Key Research and Development Program of Guangdong [2019B020221002]
- National Key Research and Development Program of China [2018 YFD0200303]
- Natural Science Foundation of China [51875217]
- Science Foundation of Guangdong for Distinguished Young Scholars [2019B151502056]
- Earmarked Fund for Modern Agro-Industry Technology Research System [CARS-01-43]
- National Science Foundation for Young Scientists of China [31801258]
- Science and Technology Research Program of Guangzhou [201803020021]
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 degrees 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.
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