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

Abstract

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.

Keywords

• Zea mays
• artificial intelligence
• evapotranspiration
• machine learning
• smart irrigation