Development of an Automated Pan Evaporation Measurement and Precision Irrigation System for Crop Production

Automated pan evaporation (APEV) measurement based on Internet of Things (IoT)–enabled sensors and precision irrigation (PI) for crop production is not commonly used in crop fields, and its performance has not yet been studied in developing countries. Soil sensors are costly and inaccurate in variable soils. Similarly, traditional pan evaporimeters require labor‐intensive manual measurements. Therefore, the objective of this study was to develop and test the APEV measurement using an IoT‐enabled sensor and PI for crop production. The IoT‐based APEV measurement system for PI was developed, tested, and evaluated for eggplant production under field conditions. The field evaluation test was conducted in a randomized block design with two treatments and two replication blocks. The treatments consisted of APEV measurement and automated irrigation (IR) with a drip system ( T ₁ ) and conventional surface flooding IR applied up to field capacity ( T ₂ ). The treatment T ₁ system was structured into three key components: (i) hardware, (ii) software and mobile app integration, and (iii) field validation of the developed system. The hardware system consisted of an ESP32, a solenoid valve, an ultrasonic sensor, a pan evaporimeter, a water flow sensor, and water supply‐related drip IR accessories. The T ₁ system consisted of an ultrasonic sensor, a sensor node for collecting real‐time APEV values from the field, and an automation node for controlling IR. An IoT‐based mobile app (Blynk) was used for real‐time monitoring and operating the IR system’s switch on or off, while ThingSpeak enabled data storage, analysis, and real‐time data visualization and controlled the IR system. The T ₁ system automatically measured the water level from the pan evaporimeter and supplied water to the crop field without manual intervention. The T ₁ system produced a nearly identical yield, saved IR water by 50.6%, and achieved an IR water use efficiency (IWUE) and crop water use efficiency (WUE) by 17.9 and 16.6 kg/m ³ , compared with 9.0 and 8.3 kg/m ³ , respectively, under the traditional surface flooding IR system ( T ₂ ). This newly developed IoT‐enabled PI based on the APEV IR method, using an ultrasonic sensor, needs further validation across diverse field conditions.