Efficient selection of drought-tolerant crops requires identification and high-throughput phenotyping (HTP) of the complex functional (especially canopy-conductance) traits that elicit plant responses to continually fluctuating environmental conditions. However, phenotyping of such dynamic physiology-based traits has been immensely challenging especially due to the limited availability of adequate methods that can provide continuous measurements of plant-water relations. Therefore, gravimetric phenotyping of plants is being increasingly used to allow one-to-one monitoring of plant-water relations and generate continuous evapotranspiration (ET) profiles. The gravimetric sensors or load cells can provide ET estimates at very high frequencies, e.g. 15-min interval, as chosen by the user. There is however, no study on understanding the optimum frequency or the sampling time at which ET needs to be monitored, such that data-redundancy, noise and processing overhead could be reduced. Hence, this paper makes a novel attempt in identifying the optimum sampling time for phenotyping ET from load cells time series. The proposed procedure includes an ensemble Machine-Learning (ML) approach for optimizing the sampling time through time series forecasting of ET profiles and classification of genotypes using the forecasted ET values. High-frequency load cells data from the LeasyScan, HTP platform, ICRISAT were used to derive the ET profiles at frequencies or scales varying from 15-min to 180-min, followed by ET forecasting and classification at each frequency. For both forecasting and classification, an ensemble of three ML algorithms i.e. Support Vector Machines (SVM), Artificial Neural Network (ANN) and Random Forests (RF) were leveraged. Consequently, the performance metrics (of both the operations) obtained from the ensemble were used to compute the entropy-based optimum sampling time. The results reveal that 60-min interval HTP data could be credibly used for both, forecasting ET as well as correctly classifying the genotypes.

有效选择耐旱作物需要对引起植物对持续变化的环境条件做出响应的复杂功能（尤其是冠层传导）性状进行识别和高通量表型（HTP）。然而，基于动态生理学性状的表型研究面临巨大挑战，特别是由于有限的可提供植物水关系连续测量方法的可用性有限。因此，越来越多地使用植物的重量表型来对植物与水的关系进行一对一监测，并产生连续的蒸散量（ET）。重量传感器或称重传感器可以按用户选择的非常高的频率（例如15分钟间隔）提供ET估计值。但是有 没有研究了解需要监视ET的最佳频率或采样时间，从而可以减少数据冗余，噪声和处理开销。因此，本文进行了新颖的尝试，即从称重传感器时间序列中确定用于表型ET的最佳采样时间。拟议的程序包括整体机器学习（ML）方法，该方法可通过对ET分布图进行时间序列预测并使用预测的ET值对基因型进行分类来优化采样时间。使用来自LeasyScan，HTP平台和ICRISAT的高频称重传感器数据，以15至180分钟不等的频率或比例导出ET曲线，然后对每个频率进行ET预测和分类。对于预测和分类，这是三种ML算法的集合，即 利用支持向量机（SVM），人工神经网络（ANN）和随机森林（RF）。因此，从集合中获得的（两个操作的）性能指标都用于计算基于熵的最佳采样时间。结果表明，可以可靠地将60分钟间隔的HTP数据用于预测ET和正确分类基因型。