This work considers the Internet of Things (IoT) and machine learning (ML) applied to the agricultural sector within a real-working scenario. More specifically, the aim is to punctually forecast two of the most important meteorological parameters (solar radiation and the rainfall) to determine the amount of water needed by a specific plantation under different contour conditions. Three different state-of-the-art ML approaches, coupled with boosting techniques, have been adopted and compared to obtain hourly forecasting. Real-working conditions are referred to the situation in which training data are missing for a specific weather station near the specific field to be irrigated. A simple but effective approach, based on correlation between available weather stations, is considered to cope with this problem. Results, evaluated considering different metrics as well as the execution time, demonstrate the viability of the proposed solution in real IoT working scenario in which these forecasting are input data to successively evaluate irrigation needing.

这项工作考虑了在实际工作场景中应用于农业部门的物联网 (IoT) 和机器学习 (ML)。更具体地说，目的是准时预测两个最重要的气象参数（太阳辐射和降雨量），以确定特定种植园在不同等高线条件下所需的水量。已经采用并比较了三种不同的最先进的 ML 方法，再加上提升技术，以获得每小时预测。实际工作条件是指在待灌溉的特定田地附近的特定气象站缺少训练数据的情况。一种基于可用气象站之间相关性的简单但有效的方法被认为可以解决这个问题。结果，