Estimation of aboveground biomass of potato based on ground hyperspectral

2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) Pub Date : 2019-07-01 DOI:10.1109/Agro-Geoinformatics.2019.8820542

Haojie Pei, Haikuan Feng, Changchun Li, Guijun Yang, Zhichao Wu, Mingxing Liu

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引用次数: 2

Abstract

Biomass is an important indicator of crop population characteristics and growth monitoring. Rapid and accurate monitoring of crop biomass is important for precise management of farmland. The spectral indices of the combination of any two bands of 350~2500nm were obtained that have good correlation with biomass were screened out through correlation analysis. At the same time, they were as input variables of biomass estimation models. Above-biomass of potato estimation models were established with partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF). The result showed the potato tuber formation period and the tuber growth period, the combination index using the PLSR method to construct the potato biomass estimation model is higher, the starch accumulation period and the mature period, the combination index using MLR method to construct the biomass estimation model is high, can be better to realize the potato biomass estimation

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基于地面高光谱的马铃薯地上生物量估算

生物量是作物种群特征和生长监测的重要指标。快速、准确地监测作物生物量对农田的精确管理具有重要意义。在350~2500nm范围内得到任意两个波段组合的光谱指数，通过相关分析筛选出与生物量相关性较好的光谱指数。同时，它们作为生物量估算模型的输入变量。采用偏最小二乘回归(PLSR)、多元线性回归(MLR)和随机森林(RF)建立了马铃薯生物量估算模型。结果表明，马铃薯块茎形成期和块茎生长期，采用PLSR方法构建的马铃薯生物量估算模型的组合指数较高，淀粉积累期和成熟期，采用MLR方法构建的生物量估算模型的组合指数较高，能较好地实现马铃薯生物量估算

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2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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