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Journal of Integrative Agriculture
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Estimation model of potassium content in cotton leaves based on hyperspectral information of multileaf position
Qiushuang Yao*, Huihan Wang*, Ze Zhang#, Shizhe Qin, Lulu Ma, Xiangyu Chen, Hongyu Wang, Lu Wang, Xin Lv 1#

The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832000, China

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摘要  钾(K)作为一种流动性强的营养元素,可以通过再分配不断调整棉花叶间与叶内对K需求的适应策略,间接导致了不同叶位叶片钾含量(LKC, %)的丰缺变化。然而,受光照和叶龄的相互作用,不同叶位叶片对这种变化的敏感程度并不相同,也包括对光谱的反射和吸收。如何选择最佳监测叶位是利用光谱遥感技术快速准确评估棉花LKC的一个重要因素。因此,本研究基于棉花自上而下叶位LKC的垂直分布特征,提出一种多叶位综合估算模型,实现准确估算棉花LKC的同时优化监测叶位的选择策略。连续2年(2020-2021年),我们采集了棉花蕾期、花期和铃期自上而下全部叶位主茎叶片(Li, i=1, 2, 3,...n)的高光谱成像数据。研究不同叶位LKC的垂直分布特征,敏感性差异以及与光谱之间的相关关系,确定最佳监测优势叶位范围;利用偏最小二乘(PLSR)、随机森林回归(RFR)、支持向量机回归(SVR)以及熵权法(EWM)分别建立了单叶位和多叶位LKC估算模型。结果表明,棉花LKC呈垂直异质性分布,LKC自上而下呈先增加后缓慢减少的趋势,平均LKC在开花期达到最大值。上部叶位叶片对K的敏感性更强,与光谱的相关性更好。三个生育时期选择的监测优势叶位范围分别为L1-L5, L1-L4以及L1-L2。基于监测优势叶位,三个生育期估算LKC的最佳单叶位模型分别为PLSR-CARS-L4, PLSR-RF-L1SVR-RF-L2R2val分别为0.786, 0.580.768RMSEval分别为0.168, 0.1970.191;利用EWM构建多叶位置LKC估计模型,R2val分别为0.887, 0.7280.703RMSEval分别为0.134, 0.1720.209。相比之下,新开发的多叶位综合估算模型取得较好结果,在精度较高的基础上提高了模型的稳定性,尤其是在蕾期和花期。这些结果对棉花LKC光谱模型的研究及选择适合的田间监测叶位具有重要意义。

Abstract  Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution.  This indirectly leads to variations in the leaf potassium content (LKC, %) at different leaf positions.  However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum.  How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology.  Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom.  This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, ... , n), during the cotton budding, flowering, and boll setting stages.  Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated.  Additionally, the optimal range of the dominant leaf position for monitoring was determined.  Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions.  The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage.  The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.  The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively.  Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R2val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively.  The multileaf position LKC estimation model was constructed by EWM with R2val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively.  In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages.  These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.
Keywords:  hyperspectral       vertical heterogeneity              leaf position              cotton              LKC  
Online: 25 March 2024  
About author:  Qiushuang Yao, E-mail: 20182012108@stu.shzu.edu.cn; Huihan Wang, E-mail: 20192012028@stu.shzu.edu.cn; #Correspondence Ze Zhang, Tel: +86-993-2058751, E-mail: zhangze1227@shzu.edu.cn; LV Xin, Tel: +86-993-2058656, E-mail: luxin@shzu.edu.cn

Cite this article: 

Qiushuang Yao, Huihan Wang, Ze Zhang, Shizhe Qin, Lulu Ma, Xiangyu Chen, Hongyu Wang, Lu Wang, Xin Lv. 2024. Estimation model of potassium content in cotton leaves based on hyperspectral information of multileaf position. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2024.03.012

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