Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy
Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/1/39 |
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| author | Jing Xiao Yuan Zhang Xin Du Qiangzi Li Hongyan Wang Yueting Wang Jingyuan Xu Yong Dong Yunqi Shen Sifeng Yan Shuguang Gong Haoxuan Hu |
| author_facet | Jing Xiao Yuan Zhang Xin Du Qiangzi Li Hongyan Wang Yueting Wang Jingyuan Xu Yong Dong Yunqi Shen Sifeng Yan Shuguang Gong Haoxuan Hu |
| author_sort | Jing Xiao |
| collection | DOAJ |
| description | Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages. By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R<sup>2</sup> = 0.602, RMSE = 0.094). Our approach improves performance over single-temporal models, and successful maize density maps were generated for the three typical demonstration counties. This work represents an advancement in large-scale crop density estimation, with the potential to expand to other regions and support precision agriculture efforts, offering a foundation for future research on optimizing agricultural resource management. |
| format | Article |
| id | doaj-art-30783cd5ed02429bbb8e2d98d97d64d2 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-30783cd5ed02429bbb8e2d98d97d64d22025-01-10T13:19:34ZengMDPI AGPlants2223-77472024-12-011413910.3390/plants14010039Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application StrategyJing Xiao0Yuan Zhang1Xin Du2Qiangzi Li3Hongyan Wang4Yueting Wang5Jingyuan Xu6Yong Dong7Yunqi Shen8Sifeng Yan9Shuguang Gong10Haoxuan Hu11Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAccurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages. By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R<sup>2</sup> = 0.602, RMSE = 0.094). Our approach improves performance over single-temporal models, and successful maize density maps were generated for the three typical demonstration counties. This work represents an advancement in large-scale crop density estimation, with the potential to expand to other regions and support precision agriculture efforts, offering a foundation for future research on optimizing agricultural resource management.https://www.mdpi.com/2223-7747/14/1/39maize density estimationmapping strategylarge-scalemulti-temporal |
| spellingShingle | Jing Xiao Yuan Zhang Xin Du Qiangzi Li Hongyan Wang Yueting Wang Jingyuan Xu Yong Dong Yunqi Shen Sifeng Yan Shuguang Gong Haoxuan Hu Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy Plants maize density estimation mapping strategy large-scale multi-temporal |
| title | Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy |
| title_full | Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy |
| title_fullStr | Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy |
| title_full_unstemmed | Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy |
| title_short | Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy |
| title_sort | large scale mapping of maize plant density using multi temporal optical and radar data models potential and application strategy |
| topic | maize density estimation mapping strategy large-scale multi-temporal |
| url | https://www.mdpi.com/2223-7747/14/1/39 |
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