English abstract
One of the requirements that must be met in the production of controlled environment agriculture (CEA) is the continuous monitoring of the plant's physiological properties and internal environment parameters. CEA, with the Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies, provides high-technology tools for automatically monitoring and detecting CEA microclimate and the plant's physiology condition. Additionally, remote sensing technology, including drone applications, is considered a critical technology that can be adapted to CEA. Since estimating canopy temperature (Tc) is a crucial step in monitoring plant water status, this research aimed to investigate the effectiveness of remote sensing via a drone-based thermal /infrared camera in enhancing the performance of CEA and monitoring plant health. In this study, we developed a technique for assessing Tc via thermocouple and thermal imaging for pepper plants in a CEA to identify plants that suffer from water stress and verify its potential to be used as a technology to assess sweet pepper water status using thermal imaging. For the microclimate inside the CEA, we used several WSN sensors (air temperature/Relative humidity, Soil moisture content, Solar radiation, and Photosynthesis). Furthermore, we evaluated the influence of three irrigation levels ranging from 50% ETc, 150% ETc, and 200% ETc in comparison to 100%ETc C (control), which was the sufficient amount of water used for the plants. The results revealed that the microclimate inside CEA was proper for plant growth by detecting internal parameters. However, thermal analysis showed that all treatments were significantly different from the control (C). Although, T2 and T3 were not significantly different from each other. The overall correlation of Tc between drone-based thermal images and in-situ sensor-based detection was excellent with an R2 of 0.9587. Crop Water Stress Index (CWSI) was calculated based on Tc extracted from thermal images and then used for crop water deficit diagnosis. The results indicated that the approach used to calculate CWSI can effectively measure plant stress, which helps farmers decide on irrigation schedules. The findings showed a positive relationship between the plant's CWSI and its Tc at midday, as well as the relationship between soil moisture content and CWSI. In general, the technique utilized in this study has the potential to enhance irrigation scheduling and provide accurate crop water requirements in specific spatial and temporal dimensions.
