Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment
Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are oft...
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500005X |
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| author | Jian Wang Zhenggui Zhang Xin Li Lu Feng Xiaofei Li Minghua Xin Shiwu Xiong Yingchun Han Shijie Zhang Xiaoyu Zhi Beifang Yang Guoping Wang Yaping Lei Zhanbiao Wang Yabing Li |
| author_facet | Jian Wang Zhenggui Zhang Xin Li Lu Feng Xiaofei Li Minghua Xin Shiwu Xiong Yingchun Han Shijie Zhang Xiaoyu Zhi Beifang Yang Guoping Wang Yaping Lei Zhanbiao Wang Yabing Li |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (pH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for pH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with pH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity. |
| format | Article |
| id | doaj-art-1f6e9f25c87c4a789a202cc5580105db |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-1f6e9f25c87c4a789a202cc5580105db2025-02-11T04:35:40ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100771Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessmentJian Wang0Zhenggui Zhang1Xin Li2Lu Feng3Xiaofei Li4Minghua Xin5Shiwu Xiong6Yingchun Han7Shijie Zhang8Xiaoyu Zhi9Beifang Yang10Guoping Wang11Yaping Lei12Zhanbiao Wang13Yabing Li14State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Institute of Western Agriculture, the Chinese Academy of Agicultural Sciences, Changji 831100, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Institute of Western Agriculture, the Chinese Academy of Agicultural Sciences, Changji 831100, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Institute of Western Agriculture, the Chinese Academy of Agicultural Sciences, Changji 831100, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR ChinaState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Institute of Western Agriculture, the Chinese Academy of Agicultural Sciences, Changji 831100, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR China; Corresponding author.State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan 455000, PR China; Institute of Western Agriculture, the Chinese Academy of Agicultural Sciences, Changji 831100, PR China; Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Henan, Zhengzhou 450001, PR China; Corresponding author.Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (pH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for pH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with pH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity.http://www.sciencedirect.com/science/article/pii/S277237552500005XCrop growth monitoringCanopy structurePAR interception ratePhotometric sensorsSpatial variation |
| spellingShingle | Jian Wang Zhenggui Zhang Xin Li Lu Feng Xiaofei Li Minghua Xin Shiwu Xiong Yingchun Han Shijie Zhang Xiaoyu Zhi Beifang Yang Guoping Wang Yaping Lei Zhanbiao Wang Yabing Li Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment Smart Agricultural Technology Crop growth monitoring Canopy structure PAR interception rate Photometric sensors Spatial variation |
| title | Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment |
| title_full | Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment |
| title_fullStr | Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment |
| title_full_unstemmed | Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment |
| title_short | Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment |
| title_sort | photon sensor based monitoring of spatial variations in canopy fipar for crop growth assessment |
| topic | Crop growth monitoring Canopy structure PAR interception rate Photometric sensors Spatial variation |
| url | http://www.sciencedirect.com/science/article/pii/S277237552500005X |
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