ABSTRACT Real-time monitoring of the nutritional status of potato crops enables rational and efficient decision-making about planting patterns and fertilization strategies that maximize yields. Chlorophyll is a useful index for measuring potatoes’ nutritional status. Therefore, rapid and accurate estimation of chlorophyll content can be used to guide efforts to improve potato crop quality and yields. Here, we use hyperspectral potato crop data collected by an unmanned aerial vehicle (UAV) and correlate the data with vegetation indexes, spectral position, and area characteristic parameters, spectral resolution, and other index parameters to comprehensively analyse the chlorophyll content of experimental potato crops at different growth stages. We establish a model for estimating chlorophyll content and verify the accuracy of the model by using partial least squares, stepwise regression analysis, support vector machine, and random forest analytical methods. This study provides a new method for estimating the chlorophyll content of crops by using hyperspectral data. We find that the partial least squares (PLS) model based on hyperspectral reflection characteristic variables is optimal for estimating chlorophyll content during the budding and tuber stages of potato growth. The optimal model during tuber formation and starch accumulation is the stepwise regression model on the basis of vegetation indexes and spectral position and area characteristic parameters. Comprehensive results show that compared with the single index parameter, the comprehensive index parameter can be used to estimate the chlorophyll content of potatoes with higher accuracy and better effect; it can also be used to monitor the nutritional status of potatoes.

中文翻译：

---

利用无人机采集的复合高光谱指数参数估计马铃薯叶绿素含量

摘要 实时监测马铃薯作物的营养状况，可以对种植模式和施肥策略做出合理有效的决策，从而最大限度地提高产量。叶绿素是衡量马铃薯营养状况的有用指标。因此，叶绿素含量的快速和准确估计可用于指导努力提高马铃薯作物的质量和产量。在这里，我们利用无人机（UAV）采集的高光谱马铃薯作物数据，将数据与植被指数、光谱位置和区域特征参数、光谱分辨率等指标参数关联起来，综合分析试验马铃薯作物的叶绿素含量。在不同的成长阶段。我们建立了叶绿素含量估计模型，并通过偏最小二乘法、逐步回归分析、支持向量机和随机森林分析方法验证了模型的准确性。本研究为利用高光谱数据估算作物叶绿素含量提供了一种新方法。我们发现基于高光谱反射特征变量的偏最小二乘 (PLS) 模型最适合估计马铃薯生长出芽和块茎阶段的叶绿素含量。块茎形成和淀粉积累过程中的最优模型是基于植被指数和光谱位置和区域特征参数的逐步回归模型。综合结果表明，与单一指标参数相比，综合指标参数可用于估算马铃薯叶绿素含量，精度更高，效果更好；它还可以用于监测马铃薯的营养状况。