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首页> 外文会议>Conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping;Society of Photo-Optical Instrumentation Engineers >Estimation of soil moisture at dierent soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery
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Estimation of soil moisture at dierent soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery

机译：使用机器学习技术和无人机（UAV）多光谱图像估算不同土壤水平下的土壤水分

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Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parametersthat are not easily measured spatially, such as soil texture and soil type. Thus, several studies have beenconducted on the estimation of soil moisture using remotely sensed data and data mining techniques such asarticial neural networks (ANNs) and support vector machines (SVMs). However, all models developed basedon these techniques are limited to site-specific applications where they are trained and their parameters aretuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learningprocess are not accessible to researchers, each application of these machine learning approaches must repeat thesetraining steps for any new study area. The fact that the results of this machine learning, black box approachcannot be easily transferred to different locations for extraction of soil moisture estimates is frustrating, andit can lead to inaccurate comparisons between methods or model performance. To overcome the Black-boxissue, this study employed a powerful technique called genetic programming (GP), which is a combination of anevolutionary algorithm and artificial intelligence, to simulate soil moisture at different levels using high-resolution,multispectral imagery acquired with an unmanned aerial vehicle (UAV). The output of this approach is eithera linear or nonlinear empirical equation that can be used by others. The performance of GP was comparedwith ANN and SVM modeling results. Several sets of high-resolution aerial imagery captured by the Utah StateUniversity AggieAir UAV system over two experimental pasture sites located in northern and southern Utah wereused for this soil moisture estimation approach. The inputs used to train these models include the reectance forthe visible, near-infrared (NIR), and thermal bands. The results show (1) the performance of GP versus ANNand SVM and (2) the master equation provided by GP, which can be used in other locations and applications.
机译：土壤水分是水平衡模型的关键组成部分。从物理上讲，它是参数的非线性函数 不容易在空间上测量的参数，例如土壤质地和土壤类型。因此，已经进行了一些研究。 使用遥感数据和数据挖掘技术对土壤水分进行估算，例如 人工神经网络（ANN）和支持向量机（SVM）。但是，所有模型都是根据 这些技术的使用仅限于特定现场的应用程序，在这些应用程序上它们经过培训并且其参数 调优。而且，由于由机器学习产生并进行的非线性方程组 研究人员无法访问该过程，因此这些机器学习方法的每个应用都必须重复这些过程 任何新学习领域的培训步骤。事实证明，这种机器学习的黑匣子方法 无法轻易地转移到不同的位置以提取土壤水分，这令人感到沮丧，并且 它可能导致方法或模型性能之间的比较不准确。克服黑匣子 问题是，这项研究采用了一种称为基因编程（GP）的强大技术，该技术结合了 进化算法和人工智能，以高分辨率模拟不同水平的土壤湿度， 使用无人机（UAV）采集的多光谱图像。这种方法的输出是 其他人可以使用的线性或非线性经验方程式。比较了GP的性能 具有ANN和SVM建模结果。犹他州捕获的几组高分辨率航空影像 在位于犹他州北部和南部的两个实验性牧场上的大学AggieAir无人机系统分别是 用于这种土壤水分估算方法。用于训练这些模型的输入包括 对 可见，近红外（NIR）和热带。结果表明（1）GP与ANN的性能 （2）GP提供的主方程，可以在其他位置和应用中使用。

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