Xia Q, Fu L J, Tang H, Song L, Tan J L, Guo Y
Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of IoT, Jiangnan University, 214122 Wuxi, China.
Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University, 225009 Yangzhou, China.
Photosynthetica. 2022 Feb 28;60(1):102-109. doi: 10.32615/ps.2022.005. eCollection 2022.
Sensing and classification of drought stress levels are very important to agricultural production. In this work, rice drought stress levels were classified based on the commonly used chlorophyll fluorescence (ChlF) parameter (F/F), feature data (induction features), and the whole OJIP induction (induction curve) by using a Support Vector Machine (SVM). The classification accuracies were compared with those obtained by the K-Nearest Neighbors (KNN) and the Ensemble model (Ensemble) correspondingly. The results show that the SVM can be used to classify drought stress levels of rice more accurately compared to the KNN and the Ensemble and the classification accuracy (86.7%) for the induction curve as input is higher than the accuracy (43.9%) with F/F as input and the accuracy (72.7%) with induction features as input. The results imply that the induction curve carries important information on plant physiology. This work provides a method of determining rice drought stress levels based on ChlF.
干旱胁迫水平的感知和分类对农业生产非常重要。在这项工作中，利用支持向量机（SVM），基于常用的叶绿素荧光（ChlF）参数（F/F）、特征数据（诱导特征）以及整个OJIP诱导过程（诱导曲线）对水稻干旱胁迫水平进行分类。相应地，将分类准确率与通过K近邻算法（KNN）和集成模型（Ensemble）获得的准确率进行比较。结果表明，与KNN和Ensemble相比，SVM能够更准确地对水稻干旱胁迫水平进行分类，以诱导曲线作为输入的分类准确率（86.7%）高于以F/F作为输入的准确率（43.9%）以及以诱导特征作为输入的准确率（72.7%）。结果表明，诱导曲线携带了关于植物生理的重要信息。这项工作提供了一种基于叶绿素荧光确定水稻干旱胁迫水平的方法。
