Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field
The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was co...
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| author | Hanyu Ma Weiliang Wen Wenbo Gou Yuqiang Liang Minggang Zhang Jiangchuan Fan Shenghao Gu Dongsheng Zhang Xinyu Guo |
| author_facet | Hanyu Ma Weiliang Wen Wenbo Gou Yuqiang Liang Minggang Zhang Jiangchuan Fan Shenghao Gu Dongsheng Zhang Xinyu Guo |
| author_sort | Hanyu Ma |
| collection | DOAJ |
| description | The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (H<sub>max</sub>), mean height (H<sub>mean</sub>), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (ME<sub>H</sub>) was achieved. The results showed that the average mIoU, mP, mR, and mF<sub>1</sub> of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF<sub>1</sub> of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the ME<sub>H</sub> of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP. |
| format | Article |
| id | doaj-art-d7a5d405c9724a1ebb5770d1d2eab717 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d7a5d405c9724a1ebb5770d1d2eab7172025-01-10T13:13:21ZengMDPI AGAgriculture2077-04722024-12-01151610.3390/agriculture15010006Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in FieldHanyu Ma0Weiliang Wen1Wenbo Gou2Yuqiang Liang3Minggang Zhang4Jiangchuan Fan5Shenghao Gu6Dongsheng Zhang7Xinyu Guo8College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Agriculture, Shanxi Agricultural University, Jinzhong 030801, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaThe spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (H<sub>max</sub>), mean height (H<sub>mean</sub>), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (ME<sub>H</sub>) was achieved. The results showed that the average mIoU, mP, mR, and mF<sub>1</sub> of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF<sub>1</sub> of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the ME<sub>H</sub> of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP.https://www.mdpi.com/2077-0472/15/1/6maize canopytime-series phenotype3D point cloudplot segmentationmarginal effect |
| spellingShingle | Hanyu Ma Weiliang Wen Wenbo Gou Yuqiang Liang Minggang Zhang Jiangchuan Fan Shenghao Gu Dongsheng Zhang Xinyu Guo Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field Agriculture maize canopy time-series phenotype 3D point cloud plot segmentation marginal effect |
| title | Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field |
| title_full | Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field |
| title_fullStr | Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field |
| title_full_unstemmed | Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field |
| title_short | Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field |
| title_sort | three dimensional time series monitoring of maize canopy structure using rail driven plant phenotyping platform in field |
| topic | maize canopy time-series phenotype 3D point cloud plot segmentation marginal effect |
| url | https://www.mdpi.com/2077-0472/15/1/6 |
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