Fertigation management for sustainable precision agriculture based on Internet of Things

Internet of Things (IoT) has played a key role in developing sustainable precision agriculture. This study addresses water and fertilizer allocation issues derived from the IoT-enabled precision agriculture for achieving sustainable irrigation and fertilization management. Existing studies on irrigation and fertilization management have more focused on short-term management and valued the timeliness of resource scheduling. However, short-term management is unsustainable since it ignores the economic and environmental goals of production activities and not applicable when the resources are limited. To fill this gap, this study develops a framework for the IoT-based irrigation and fertilization system in which both long-term and short-term planning are considered. Based on the framework, an integer linear programming model is developed for allocating limited resources among multiple crops with the goal of maximizing the economic profits and environmental benefits. After that, a hybrid genetic algorithm is designed to solve the optimization model. Finally, numerical experiments based on a case study are conducted to test the effectiveness of the proposed model and solving method. Results have confirmed that the optimization model presented in this study can promote sustainable irrigation and fertilization management in precision agriculture by offering more economic and environmental benefits than empirical models. Also, related management implications are obtained from sensitivity analysis to support the decision-making of managers, involving planting structure design, strategies selection of water and fertilizer storage and replenishment. (C) 2020 Elsevier Ltd. All rights reserved.