Plant water stress monitoring and control system

Precision irrigation is an important component of the water-saving approaches in agriculture and is one of the methods for increasing water-saving efficiency. Detecting water stress in crops accurately is the basis of precision irrigation techniques. In this study, the crop water stress monitoring system was developed to detect the acoustic emission (AE) of plants, associating the three factors of soil, crops, and weather in real-time. The automatic mon-itoring system, which was developed using the virtual instrument platform, not only affects the water stress and the drought of the atmosphere but can also be used to control the greenhouse environment automatically. The substance subjected to testing was a potted tomato plant (Lycopersicon esculentum). Environmental parameters are monitored in this system using a virtual instrument data acquisition system based on Peripheral Component Inter-connect -Data acquisition (PCI-DAQ). The AE sensor was used to fix tomato stems at 1/3 and 2/3 of their overall height in order to detect AE information, which serves as an indirect indicator of the plant's water condition. This system, which could test and record water requirement information for the crops, has been demonstrated to be stable, nondestructive, and easily manipulable. The results showed that the cohesion between water molecules was weak under water stress conditions. The water flow fracture of the conduits resulted in cavitation by rapidly expanding gas bubbles. The cavitation event caused a rapid relaxation of the liquid tension that produced an AE of energy. It was found that crop hydraulic structure and anti-drought indicators were correlated with AE, which resulted from crop adaptation to the environment. The counts of AE change regularly, according to the variation of the environment's temperature, humidity, carbon dioxide consistency, and transpiration. Based on the relationship between AE and crop water stress conditions, the mathematical model of precision irrigation was obtained. As producing AE is a complex biological process, the future work is to build the different crops' information models in different growth periods. The data acquisition based on the AE information should be researched further.