Abstract
We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology.Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.
Highlights
Improvement of agricultural production capacity is extremely important to human beings in face of the expanding population in the 21st century (Alston et al, 2009; Godfray et al, 2010)
We developed a system for high throughput monitoring the crop physiology states in a greenhouse
Pseudo-color images of photosystem II (PSII), Fv/Fm and 550/510 parameters (Figure 3) visualize how drought stress affected the physiological status of tomato plants
Summary
Improvement of agricultural production capacity is extremely important to human beings in face of the expanding population in the 21st century (Alston et al, 2009; Godfray et al, 2010). To achieve this goal, we need modernization of agriculture which relies on breakthrough in aspects such as dynamic monitoring, intelligent control, and automatic implementation (Klukas et al, 2014; Sankaran et al, 2015). It is necessary to obtain the growth status information to guide the control of plant diseases in greenhouse and field (Stafford, 2000; Diacono et al, 2013). The information about the crop growth status should be visualized and quantified in order to offer a more intuitive and standardized reference for management of the environment (Berger et al, 2010; Virlet et al, 2014)
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