College of Engineering, Abu Dhabi University, Zayed City, Abu Dhabi P.O. Box 59911, United Arab Emirates.
Smart City Lab, NCAI (National Center of Artificial Intelligence), NED University of Engineering and Technology, Karachi 75270, Pakistan.
Sensors (Basel). 2021 Jun 17;21(12):4175. doi: 10.3390/s21124175.
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.
近年来，随着人口快速增长对粮食需求的增加，淡水供应的需求也在不断增加。巴基斯坦的农业部门贡献了其 GDP 的 26%，并雇用了整个劳动力的 43%。然而，目前使用的传统农业方法，如洪水灌溉和轮水分配系统（Warabandi），导致了过度和不合时宜的用水，以及低作物产量。基于实时农场传感器数据和智能决策支持系统的物联网 (IoT) 解决方案已经带来了许多智能农业解决方案，从而提高了水的利用效率。本研究的目的是通过部署本土开发的物联网设备和农业决策支持系统 (DSS)，在卡拉奇 Gadap 的一个 2 英亩柠檬农场测试现场比较和优化为期 9 个月的用水情况。传感器数据通过云以及移动应用程序和基于网络的信息可视化进行无线收集，DSS 系统提供灌溉建议。DSS 系统基于天气数据（温度和湿度）、部署在农场中的物联网设备的实时原位传感器数据以及作物数据（Kc 和作物类型）。这些数据被提供给彭曼-蒙蒂思和作物系数模型，以在测试现场制定灌溉计划的建议。结果表明，与传统灌溉调度在相邻 2 英亩柠檬农场中的用水和作物产量相比，节水效果显著（约 50%），同时产量也有所增加（35%），在某些恶劣条件下，该农场的温度有时会超过 50°C。
