Real‐Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human‐Machine Interactions

Abstract Real‐time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real‐time irrigation scheduling requires reliable present soil‐crop‐atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer‐based tools are rarely connected in a closed‐loop and farmers' feedback are usually not incorporated into a real‐time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real‐time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human‐machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human‐computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers' own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers' direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers' feedbacks show interest in using the tool in real‐world irrigation scheduling and providing meaningful suggestions to improve the tool for real‐world application.