Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images
Growth monitoring of rice is of great significance to food security of human society. Rice flowering is an important growing stage for grain formation, and flower intensity is the dominant factor in determining rice yield. The estimation of flower intensity helps us to know the rice yield in advance...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322300239X |
| _version_ | 1797738021666160640 |
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| author | Xiaoping Yao Qiuxiang Yi Fumin Wang Tianyue Xu Jueyi Zheng Zhou Shi |
| author_facet | Xiaoping Yao Qiuxiang Yi Fumin Wang Tianyue Xu Jueyi Zheng Zhou Shi |
| author_sort | Xiaoping Yao |
| collection | DOAJ |
| description | Growth monitoring of rice is of great significance to food security of human society. Rice flowering is an important growing stage for grain formation, and flower intensity is the dominant factor in determining rice yield. The estimation of flower intensity helps us to know the rice yield in advance. This research proposed a series of the flower index (FI) to monitor status of rice flowers by developing flower intensity estimation models using stepwise multiple linear regression (SMLR) and random forest (RF), and their performance was compared to the models developed by vegetation indices (VI) of some key growth stages. The FI that in types of normalization (NDFI), ratio (RFI) and differences (DFI) were tested. The involved FIs in the FI-based models were those in type of difference (DFI) that obtained by difference of reflectance before and after flowering (DR) and their first derivative (DR’). The FIs of ten consecutive days during the flowering were obtained and their correlations with flower intensity showed that FIs of the late flowering (the 8th day, 9th day and 10th day) were more significantly correlated to flower intensity than those at the early or mid-flowering, with the maximum correlation coefficient of 0.702 given by FIs in difference type formed by DR’. The accuracy assessment of flower intensity estimation models showed that FI-based models had the equivalent accuracies to VI-based models, especially for the SMLR model that based on four FIs, which had R2 = 0.707, MAPE = 10.54%, rRMSE = 11.39%, was comparable to the model that developed by three VIs of the booting, heading and jointing stages (R2 = 0.751, MAPE = 8.99%, rRMSE = 10.31%). The promising results of FIs in estimating flower intensity make the simple data acquisition possible and provide an alternative way to get information about rice yield. |
| first_indexed | 2024-03-12T13:37:50Z |
| format | Article |
| id | doaj.art-f0e2ec142fbf4f73b9e86b2bc67d64e0 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-12T13:37:50Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-f0e2ec142fbf4f73b9e86b2bc67d64e02023-08-24T04:34:14ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-08-01122103415Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral imagesXiaoping Yao0Qiuxiang Yi1Fumin Wang2Tianyue Xu3Jueyi Zheng4Zhou Shi5Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, China; Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, ChinaSchool of Geomatics and Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 311228, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, China; Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, China; Corresponding author.Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, China; Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, China; Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaGrowth monitoring of rice is of great significance to food security of human society. Rice flowering is an important growing stage for grain formation, and flower intensity is the dominant factor in determining rice yield. The estimation of flower intensity helps us to know the rice yield in advance. This research proposed a series of the flower index (FI) to monitor status of rice flowers by developing flower intensity estimation models using stepwise multiple linear regression (SMLR) and random forest (RF), and their performance was compared to the models developed by vegetation indices (VI) of some key growth stages. The FI that in types of normalization (NDFI), ratio (RFI) and differences (DFI) were tested. The involved FIs in the FI-based models were those in type of difference (DFI) that obtained by difference of reflectance before and after flowering (DR) and their first derivative (DR’). The FIs of ten consecutive days during the flowering were obtained and their correlations with flower intensity showed that FIs of the late flowering (the 8th day, 9th day and 10th day) were more significantly correlated to flower intensity than those at the early or mid-flowering, with the maximum correlation coefficient of 0.702 given by FIs in difference type formed by DR’. The accuracy assessment of flower intensity estimation models showed that FI-based models had the equivalent accuracies to VI-based models, especially for the SMLR model that based on four FIs, which had R2 = 0.707, MAPE = 10.54%, rRMSE = 11.39%, was comparable to the model that developed by three VIs of the booting, heading and jointing stages (R2 = 0.751, MAPE = 8.99%, rRMSE = 10.31%). The promising results of FIs in estimating flower intensity make the simple data acquisition possible and provide an alternative way to get information about rice yield.http://www.sciencedirect.com/science/article/pii/S156984322300239XRiceFlower intensityVegetation indexFlower indexUnmanned aerial vehicle (UAV) |
| spellingShingle | Xiaoping Yao Qiuxiang Yi Fumin Wang Tianyue Xu Jueyi Zheng Zhou Shi Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images International Journal of Applied Earth Observations and Geoinformation Rice Flower intensity Vegetation index Flower index Unmanned aerial vehicle (UAV) |
| title | Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images |
| title_full | Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images |
| title_fullStr | Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images |
| title_full_unstemmed | Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images |
| title_short | Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images |
| title_sort | estimating rice flower intensity using flower spectral information from unmanned aerial vehicle uav hyperspectral images |
| topic | Rice Flower intensity Vegetation index Flower index Unmanned aerial vehicle (UAV) |
| url | http://www.sciencedirect.com/science/article/pii/S156984322300239X |
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