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 a fluorescence (ChlF) parameter (Fv/Fm), 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 Fv/Fm 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.

Department of Bioengineering University of Missouri Columbia MO 65211 USA

Joint International Research Laboratory of Agriculture and Agri Product Safety Yangzhou University 225009 Yangzhou China

Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of IoT Jiangnan University 214122 Wuxi China

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