外文摘要：Reported technological tools for irrigation scheduling lack the integration of real-time crop measurements, weather forecasting, and the limitations and variabilities introduced by human operation. Moreover, most of these tools do not provide practical irrigation recommendations, limiting their adoption and benefits in enhancing agricultural efficiency and reducing environmental impact. To address these challenges, we propose an adaptive and predictive irrigation management decision support system by formulating a feedback plus feedforward algorithm that uses modeling, estimation, prediction, and control strategies. The major feature of this DSS is the consideration of human intervention in the control loop. The DSS integrates field data, including soil moisture, rain, temperature, and irrigation, in addition to weather forecasting. It gives irrigation managers precise and practical instructions on how much to irrigate based on preferences for irrigation events. The DSS adapts in real-time to provide irrigation volume recommendations that ensure optimal soil moisture levels are maintained. Our approach includes the incorporation of a simplified control -oriented model (COM) to characterize the soil moisture dynamics, a data processing stage that makes the measured data compatible with the COM, a parameter estimation stage that guarantees an optimal adjustment of the COM parameters, a control stage that uses the parametrized COM, measured information from the crop, and weather forecasts to obtain optimal irrigation volume recommendation. We evaluated our DSS using data from a commercial sweetcorn field in South Florida, where seepage irrigation is used. Our findings show that (i) the proposed model and estimation stage offer an accurate description of the soil moisture dynamics, reaching correlation coefficients and R -squared values greater than 0.92 and 0.84 during all the evaluations, (ii) the algorithm can consistently regulate soil moisture, ensuring it remains at the desired levels reducing the risks associated with leaching and runoff, and iii) water savings can increase by 30%. Therefore, our DSS has the potential to become a standardized platform for providing optimal and practical irrigation recommendations to irrigation managers.
外文关键词：precision agriculture;Precision irrigation;Smart irrigation;irrigation scheduling;Decision support system (DSS);Human in the loop;Adaptive irrigation control;Predictive water management;Soil moisture forecast;Real-time irrigation;Irrigation control
作者：Conde, Gregory;Guzman, Sandra M;Athelly, Akshara
作者单位：Univ Florida
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：217
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台