A smartphone APP for weather-based irrigation scheduling using artificial neural networks

Abstract: The objective of this work was to develop a smartphone application (APP) for a weather-based irrigation scheduling using artificial neural networks (ANNs), as well as to validate it in a green corn (Zea mays) crop. An APP (IrriMobile) that uses ANNs based on temperature and relative humidity, or on temperature only, was developed to estimate the reference evapotranspiration (ETo). The APP and Bernardo’s methodology for irrigation scheduling, with the ETo estimated by the FAO-56 Penman-Monteith equation, were used to schedule the irrigation for a green corn crop. The performance of empirical equations to estimate ETo was also assessed. Several corn morphological and agronomic characteristics were evaluated. The APP was used in the experiment with temperature, relative humidity, and rainfall data. Its use was also simulated with temperature and rainfall data only. There was no difference for any of the green corn characteristics evaluated. ETo estimation through the APP showed a higher performance than that by the evaluated equations. The APP overestimates the irrigation requirements by 8 and 19% when using temperature and relative humidity, and temperature only, respectively.

摘要：本研究旨在开发一款基于人工神经网络（artificial neural networks, ANNs）的气象型灌溉调度智能手机应用程序（APP），并在青饲玉米（Zea mays）作物上开展验证试验。研发了一款名为IrriMobile的APP，其可基于气温与相对湿度，或仅基于气温来估算参考作物蒸发蒸腾量（reference evapotranspiration, ETo）。本研究分别采用该APP，以及基于FAO-56彭曼-蒙特斯方程估算ETo的Bernardo灌溉调度方法，为青饲玉米制定灌溉方案；同时评估了多款经验公式的ETo估算性能。研究对多项玉米形态学与农艺性状进行了测定。试验过程中，APP的输入数据包含气温、相对湿度与降雨数据；此外还模拟了仅输入气温与降雨数据的使用场景。结果显示，所有被测青饲玉米性状均无显著差异。通过APP估算的ETo性能优于本次评估的所有经验公式。当分别采用气温与相对湿度数据、仅气温数据时，该APP的灌溉需水量估算值分别偏高8%与19%。