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Determination of water stress for pepper from spectral reflections through artificial learning methods

机译：通过人工学习方法从光谱反射确定辣椒的水分胁迫

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Irrigation is one of the important issues that determine growing crops and soil quality in agriculture. With this in mind, irrigation should be done considering soil and climate factors. Plants and fruits need different amount of water every period. The amount of water that is not given is negatively affecting agriculture. In this study, it is tried to determine whether the pepper plant is in water stress by spectral reflections. For this purpose, spectral reflections from pepper leaves exposed to water stress, were recorded. Classification of these reflection data has been performed. The data set used in the study is divided into three groups. The first group (A) contains reflections from pepper leaves exposed to 100% water. The second group (B) contains reflections from pepper leaves exposed to 75% water. The third group (C) contains reflections from pepper leaves exposed to 50% water. Water stress detection consists of two steps. In the second stage, classification of the feature vectors related to the data is performed. The K nearest neighbors (KNN) and Artificial Neural Networks methods were used as the classification.
机译：灌溉是决定农业中农作物生长和土壤质量的重要问题之一。考虑到这一点，应在考虑土壤和气候因素的情况下进行灌溉。每个时期植物和水果需要不同量的水。没有提供的水量对农业产生了负面影响。在这项研究中，试图通过光谱反射来确定辣椒植物是否处于缺水状态。为此，记录了暴露于水分胁迫下的辣椒叶片的光谱反射。这些反射数据已被分类。研究中使用的数据集分为三组。第一组（A）包含来自暴露于100％水的胡椒叶的反射。第二组（B）包含来自暴露于75％水的胡椒叶的反射。第三组（C）包含来自暴露于50％水的胡椒叶的反射。水分胁迫检测包括两个步骤。在第二阶段，执行与数据相关的特征向量的分类。使用K最近邻（KNN）和人工神经网络方法进行分类。

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