Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

Abstract

Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress. INDEX TERMSChlorophyll fluorescence imaging, drought stress, feature extraction, machine learning, tomato seedlings. YAN LONG received the B.S. degree in electronic and information engineering and the M.S. degree in communication engineering from Shandong University, China, in 2005 and 2008, respectively, and the Ph.D. degree in agricultural electrification and automation from Northwest A&F University, China, in 2015. Since 2018, she has been an Associate Professor with the Mechanical and Electronic Engineering Department, Northwest A&F University. Her research interests include digital agriculture and agricultural information engineering, agricultural electronics, and automation technology. MINJUAN MA received the B.S. degree in electronic information engineering from Northwest A&F University, China, in 2019, where she is currently pursuing the M.S. degree in agricultural electrification and automation. Her research interests include digital agriculture and agricultural information engineering.

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

“…In terms of physiological functions, soil drought is very harmful to plant growth. Therefore, timely monitoring of soil water content can improve cultivation practices and plant productivity under stress conditions [4]. By combining the changes in the electrical signals of the plant itself and the changes in the external environment (soil water content, air humidity, etc.…”

“…Monitoring and measuring PSE or SCF enables early warning and diagnosis of water shortage in plants [11]. At present, the research on plant electrical signals under water stress mainly focuses on exploring the electrical signal characteristic changes when plants are subjected to water stress [12], [4] and using them as a means to achieve early stress warning and real-time evaluation [3]. By this, the relationship between plant electrical signals and plant growth states under different levels of water stress can be established.…”