Abstract

Monitoring grapevine phenology during the agricultural season is one of the most important tasks within the vine field since this is a key input for the proper planning of agricultural labor management. Traditionally, vine growers make very few phenological observations at the field level, which are extrapolated to an entire production unit, without considering the field natural spatial variability. This situation generates significant loss of agricultural inputs and energy, which makes the vine system less sustainable, because this vineyard natural spatial variability is not usually considered in the field management. In this study, two models were tested using information recollected by a meteorological weather station and a wireless sensor network (WSN) to estimate vineyard phenology in a key period such as flowering. Therefore, the general objective of this proposal is to develop a low-cost wireless sensor network (WSN) for monitoring the spatial variability of vine phenology in a commercial vineyard. Results indicated that both models presented a better estimation of vine phenology during the second season, given that the first season was affected by the ENSO "La Nin similar to a" climatic effect. However, it can be noted that the Parker model (GPV) presented better phenological estimation than the Monomolecular equation-based model (ME), when using a low-cost wireless sensor network. Based on the results, we can conclude that it is possible to develop and implement a low-cost electronic device for the monitoring of spatialized phenological events in the vineyard.