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）的天气驱动型灌溉调度智能手机应用（APP），并在鲜食玉米（Zea mays）作物中对其开展验证工作。本研究开发了一款名为IrriMobile的应用，其采用以温度与相对湿度、或仅以温度为输入变量的人工神经网络模型，用于估算参考作物蒸散量（reference evapotranspiration, ETo）。本研究分别采用该应用，以及采用FAO-56彭曼-蒙特斯（FAO-56 Penman-Monteith）方程估算ETo的伯纳多灌溉调度方法，对鲜食玉米田实施灌溉调度。同时评估了用于估算ETo的各类经验方程的性能表现。本研究对多项玉米形态学与农艺性状进行了观测评估。试验中，该应用分别结合温度、相对湿度与降雨数据运行；同时也模拟了仅使用温度与降雨数据运行该应用的场景。两种运行模式下，待测鲜食玉米的各项性状均无显著差异。基于该应用的ETo估算性能优于所评估的经验方程。当分别采用温度与相对湿度、仅采用温度数据时，该应用估算的灌溉需求量分别高估8%与19%。