Planning the Greenhouse Climatic Mapping Using an Agricultural Robot and Recurrent-Neural- Network-Based Virtual Sensors

Assuming climatic homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal agronomic decisions. Indeed, several approaches have been proposed based on installing sensors in predefined points of interest (PoIs) to obtain a better mapping of climatic conditions. However, these approaches suffer from two main problems, i.e., identifying the most significant PoIs inside the greenhouse and placing a sensor at each PoI, which may be costly and incompatible with field operations. As regards the first problem, we propose a genetic algorithm to identify the best sensing places based on the agronomic definition of zones of interest. As regards the second problem, we exploit agricultural robots to collect climatic information to train a set of virtual sensors based on recurrent neural networks. The proposed solution has been tested on a real-world dataset regarding a greenhouse in Verona (Italy).