Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage
Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-11-01
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| Series: | Plant Methods |
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| Online Access: | http://link.springer.com/article/10.1186/s13007-020-00693-3 |
| _version_ | 1818187881371402240 |
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| author | Shanjun Luo Yingbin He Qian Li Weihua Jiao Yaqiu Zhu Xihai Zhao |
| author_facet | Shanjun Luo Yingbin He Qian Li Weihua Jiao Yaqiu Zhu Xihai Zhao |
| author_sort | Shanjun Luo |
| collection | DOAJ |
| description | Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered. |
| first_indexed | 2024-12-11T23:18:04Z |
| format | Article |
| id | doaj.art-d4fa13d4e38048fda82c1bee8a2a300c |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-12-11T23:18:04Z |
| publishDate | 2020-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-d4fa13d4e38048fda82c1bee8a2a300c2022-12-22T00:46:27ZengBMCPlant Methods1746-48112020-11-0116111410.1186/s13007-020-00693-3Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stageShanjun Luo0Yingbin He1Qian Li2Weihua Jiao3Yaqiu Zhu4Xihai Zhao5Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesSchool of Economics and Management, Tiangong UniversityCenter for Agricultural and Rural Economic Research, Shandong University of Finance and EconomicsSchool of Economics and Management, Tiangong UniversityInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesAbstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.http://link.springer.com/article/10.1186/s13007-020-00693-3Yield estimationRemote sensingPotatoRelative variablesSlogistic modelWeighted growth stage |
| spellingShingle | Shanjun Luo Yingbin He Qian Li Weihua Jiao Yaqiu Zhu Xihai Zhao Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage Plant Methods Yield estimation Remote sensing Potato Relative variables Slogistic model Weighted growth stage |
| title | Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage |
| title_full | Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage |
| title_fullStr | Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage |
| title_full_unstemmed | Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage |
| title_short | Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage |
| title_sort | nondestructive estimation of potato yield using relative variables derived from multi period lai and hyperspectral data based on weighted growth stage |
| topic | Yield estimation Remote sensing Potato Relative variables Slogistic model Weighted growth stage |
| url | http://link.springer.com/article/10.1186/s13007-020-00693-3 |
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