Abstract
Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.
Highlights
Agriculture is the backbone of Pakistan’s economy, which contributes 26% of the country’s GDP and employs 43% of the total labor force [1]
Crop type and soil determines the volume of irrigation and fertilizers, whereas weather, soil moisture, humidity, and temperature govern the schedule of irrigation
A decision support system (DSS) that uses machine learning for irrigation water management was presented in [15], and a study for decision systems and its use in water management was presented by Guariso et al in [16]
Summary
Agriculture is the backbone of Pakistan’s economy, which contributes 26% of the country’s GDP and employs 43% of the total labor force [1]. The majority of farmers use outdated irrigation methods, such as flood irrigation, and rely on estimations for decision making in agricultural practices which typically result in excess water usage [6]. A fixed rotational water allocation system, known as Warabandi, is used in the region for supplying irrigation water to farmers as per a preplanned schedule [7] In this method, pre-allocated fixed volumes of irrigation water (estimated according to the crop) is provided into canals leading up to the fields, on a given day [8]. Pre-allocated fixed volumes of irrigation water (estimated according to the crop) is provided into canals leading up to the fields, on a given day [8] This method is widely used in Pakistan, India, Bangladesh, and some of the Mediterranean region [8,9]. IoT-based and wireless sensor network (WSN)-based monitoring and decision making for irrigation systems have accurately predicted irrigation scheduling in the past using measured data, thereby increasing water productivity [24,25,26,27,28,29,30,31,32,33,34]
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