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Water gives life to our parks and helps them to be lush and green. However, over-irrigation in parks has the potential to waste substantial amounts of water and may also result in seepage and leakage of nutrients into nearby streams. Therefore, it is important to implement a smart water management system in parks to conserve water resources. This paper presents a multidisciplinary approach to use the latest irrigation technologies, Internet of Things (IoT) communication system, sensor technologies, and machine learning model for better water management of parklands by optimising the irrigation requirement and operating conditions. The project uses Dual Electromagnetic (DUAL-EM) sensor to scan the parkland to visualise the distribution of moisture content in a contour map which helps in identifying the location of interest to install moisture sensors to build the park's realtime watering profile. The IoT system uses a Low Power Wide Area Network (LoRaWAN) to connect moisture sensors (MP640), and micro-weather station (ATMOS 41) to automate the data collection on the cloud for real-time data storage and monitoring. The live data of the IoT system is used with laboratory testing data to prepare a smarter decision system for irrigation via machine learning. The sprinklers that are controlled by the smarter decision system helps to dispense irrigation water as per the needs of the parkland thus, reducing wastage of water and minimising nutrients leaching into streams to protect natural habitats.
Chandrappa et al. (Wed,) studied this question.