Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++
The accurate quantification of plant types can provide a scientific basis for crop variety improvement, whereas efficient automatic classification methods greatly enhance crop management and breeding efficiency. For leafy crops such as Chinese cabbage, differences in the plant type directly affect t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1458962/full |
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| author | Chongchong Yang Chongchong Yang Lei Sun Lei Sun Jun Zhang Jun Zhang Xiaofei Fan Xiaofei Fan Dongfang Zhang Dongfang Zhang Tianyi Ren Tianyi Ren Minggeng Liu Minggeng Liu Zhiming Zhang Zhiming Zhang Wei Ma Wei Ma |
| author_facet | Chongchong Yang Chongchong Yang Lei Sun Lei Sun Jun Zhang Jun Zhang Xiaofei Fan Xiaofei Fan Dongfang Zhang Dongfang Zhang Tianyi Ren Tianyi Ren Minggeng Liu Minggeng Liu Zhiming Zhang Zhiming Zhang Wei Ma Wei Ma |
| author_sort | Chongchong Yang |
| collection | DOAJ |
| description | The accurate quantification of plant types can provide a scientific basis for crop variety improvement, whereas efficient automatic classification methods greatly enhance crop management and breeding efficiency. For leafy crops such as Chinese cabbage, differences in the plant type directly affect their growth and yield. However, in current agricultural production, the classification of Chinese cabbage plant types largely depends on manual observation and lacks scientific and unified standards. Therefore, it is crucial to develop a method that can quickly and accurately quantify and classify plant types. This study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++. First, we quantified the traits related to plant type based on the growth characteristics of Chinese cabbage. K-medoids clustering analysis was then used for the unsupervised classification of the data, and specific quantification of Chinese cabbage plant types was performed based on the classification results. Finally, we combined 1024 feature vectors with 10 custom dimensionless features and used the optimized PointNet++ model for supervised learning to achieve the automatic classification of Chinese cabbage plant types. The experimental results showed that this method had an accuracy of up to 92.4% in classifying the Chinese cabbage plant types, with an average recall of 92.5% and an average F1 score of 92.3%. |
| format | Article |
| id | doaj-art-92aae26de775459489d582c00b90f867 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-92aae26de775459489d582c00b90f8672025-01-17T06:50:47ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14589621458962Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++Chongchong Yang0Chongchong Yang1Lei Sun2Lei Sun3Jun Zhang4Jun Zhang5Xiaofei Fan6Xiaofei Fan7Dongfang Zhang8Dongfang Zhang9Tianyi Ren10Tianyi Ren11Minggeng Liu12Minggeng Liu13Zhiming Zhang14Zhiming Zhang15Wei Ma16Wei Ma17Country State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Horticulture, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCountry State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, ChinaCollege of Horticulture, Hebei Agricultural University, Baoding, ChinaThe accurate quantification of plant types can provide a scientific basis for crop variety improvement, whereas efficient automatic classification methods greatly enhance crop management and breeding efficiency. For leafy crops such as Chinese cabbage, differences in the plant type directly affect their growth and yield. However, in current agricultural production, the classification of Chinese cabbage plant types largely depends on manual observation and lacks scientific and unified standards. Therefore, it is crucial to develop a method that can quickly and accurately quantify and classify plant types. This study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++. First, we quantified the traits related to plant type based on the growth characteristics of Chinese cabbage. K-medoids clustering analysis was then used for the unsupervised classification of the data, and specific quantification of Chinese cabbage plant types was performed based on the classification results. Finally, we combined 1024 feature vectors with 10 custom dimensionless features and used the optimized PointNet++ model for supervised learning to achieve the automatic classification of Chinese cabbage plant types. The experimental results showed that this method had an accuracy of up to 92.4% in classifying the Chinese cabbage plant types, with an average recall of 92.5% and an average F1 score of 92.3%.https://www.frontiersin.org/articles/10.3389/fpls.2024.1458962/fullpoint cloud dataPointNet++Chinese cabbage plant type classificationdeep learningclustering analysis |
| spellingShingle | Chongchong Yang Chongchong Yang Lei Sun Lei Sun Jun Zhang Jun Zhang Xiaofei Fan Xiaofei Fan Dongfang Zhang Dongfang Zhang Tianyi Ren Tianyi Ren Minggeng Liu Minggeng Liu Zhiming Zhang Zhiming Zhang Wei Ma Wei Ma Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ Frontiers in Plant Science point cloud data PointNet++ Chinese cabbage plant type classification deep learning clustering analysis |
| title | Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ |
| title_full | Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ |
| title_fullStr | Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ |
| title_full_unstemmed | Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ |
| title_short | Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ |
| title_sort | research on the quantification and automatic classification method of chinese cabbage plant type based on point cloud data and pointnet |
| topic | point cloud data PointNet++ Chinese cabbage plant type classification deep learning clustering analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1458962/full |
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