Das Choudhury Sruti, Saha Sinjoy, Samal Ashok, Mazis Anastasios, Awada Tala
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States.
School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States.
Front Plant Sci. 2023 Feb 9;14:1003150. doi: 10.3389/fpls.2023.1003150. eCollection 2023.
The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.
本文介绍了两种新颖的算法，用于利用相机以可见光和高光谱两种模式捕获的图像序列来预测和传播植物中的干旱胁迫。第一种算法VisStressPredict，通过分析可见光相机在离散时间间隔捕获的图像序列，计算整体表型的时间序列，例如高度、生物量和大小，然后采用动态时间规整（DTW），一种用于测量时间序列之间相似性以进行动态表型分析的技术，来预测干旱胁迫的开始。第二种算法HyperStressPropagateNet，利用深度神经网络通过高光谱图像进行时间胁迫传播。它使用卷积神经网络将各个像素处的反射光谱分类为受胁迫或未受胁迫，以确定植物中胁迫的时间传播。由HyperStressPropagateNet计算得出的给定日期土壤含水量与受胁迫植物百分比之间的高度相关性证明了其有效性。尽管VisStressPredict和HyperStressPropagateNet在目标上存在根本差异，进而在输入图像序列和基础方法上也有所不同，但VisStressPredict计算得出的胁迫因子曲线预测的胁迫开始与HyperStressPropagateNet计算得出的植物中胁迫像素出现的日期高度相关。这两种算法在高通量植物表型平台上捕获的棉花植物图像序列数据集上进行了评估。这些算法可以推广到任何植物物种，以研究非生物胁迫对可持续农业实践的影响。
