Journal of Agronomy and Field Innovations

Background: In the absence of any measures, crop water stress has adverse effects, such as the decrease of yield and water-use efficiency in irrigated agriculture. Canopy temperature is a well-known indicator of stomatal closure and transpiration reduction and infrared thermometry allows for the uninterrupted measurement of canopy temperature. 
Objective: The study examines the development and performance of a cost-efficient method for real-time monitoring of canopy temperature, comprising features such as infrared sensing, environmental data fusion and an automatic decision-help system for irrigation.
Methods: The methods used for transmitting the data in the presented sensor network is in the form of an ESP32-based sensor network which integrates different elements such as a non-contact infrared thermometer, a temperature/relative-humidity sensor, a capacitive soil moisture probe, and a GPS module. It was seen that the Crop Stress Index (CSI), which was used to assess the importance of the canopy temperature readings obtained, is calculated by normalizing the difference between the temperatures of the canopy and the air with respect to the vapour pressure deficit. The method was carried out over the duration of 60 days and the results compared with the results obtained via handheld infrared guns and gravimetric sample tests.
Results: The results from the system show that the average absolute error amounts to 0.34°C with RMSE equal to 0.46 °C when compared to reference values with 94.6% of accuracy in detection of stress and 91.2% in irrigation decision making with lesser communications delays and technician’s involvement than in traditional monitoring systems.
Objective: Real-time information gathering regarding canopy temperature and decision making based on cloud technology can be considered as an efficient and economically viable approach for the modern automated irrigation systems.