Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data
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(2020) 16:150 Luo et al. Plant Methods https://doi.org/10.1186/s13007-020-00693-3
Open Access
RESEARCH
Nondestructive estimation of potato yield using relative variables derived from multi‑period LAI and hyperspectral data based on weighted growth stage Shanjun Luo1,2, Yingbin He1* , Qian Li3, Weihua Jiao4, Yaqiu Zhu3 and Xihai Zhao1
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 R 2 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. Keywords: Yield estimation, Remote sensing, Potato, Relative variables, Slogistic model, Weighted growth stage Background Potato (Solanum tuberosum L.), a mixed grain, forage, and vegetable crop [1], is the fourth most important crop in the world [2, 3]. Since the launch of the potato staple food strategy in 2015 in China, potato has become another major staple food crop after rice, wheat, and *Correspondence: [email protected] 1 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China Full list of author information is available at the end of the article
corn [4]. Timely forecasting potato yield data is a vital reference index for variety breeding determined by the combination of genes and growth environment [5]. The accurate prediction of potato yield, especially at the regional level, is of great significance for ensuring food security and promoting the sustainable development of agriculture, which is related to the formulation of major policies and guidelines of the national economy and people’s
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