PHOTOSYNTHETICA

Photosynthetica 2022, 60(1):102-109 | DOI: 10.32615/ps.2022.005

Sensing and classification of rice (Oryza sativa L.) drought stress levels based on chlorophyll fluorescence

Q. XIA¹, L.J. FU¹, H. TANG¹, L. SONG², J.L. TAN³, Y. GUO^{1, 3}

¹ Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of IoT, Jiangnan University, 214122 Wuxi, China

² Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University, 225009 Yangzhou, China

³ Department of Bioengineering, University of Missouri, Columbia, MO 65211, USA

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 (F_v/F_m), 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_v/F_m 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.

Additional key words: chlorophyll a fluorescence; drought stress; Ensemble model; K-Nearest Neighbors; Support vector machine.

Received: July 21, 2021; Revised: January 4, 2022; Accepted: January 25, 2022; Prepublished online: February 28, 2022; Published: March 18, 2022  Show citation