Use of satellite vegetation indices to predict yield levels of Vicia villosa Roth
DOI:
https://doi.org/10.24215/15146774e076Keywords:
model, prediction, yield, legumeAbstract
In this paper, satellite images, machine learning algorithms and field measurements are combined to analyze the possibility of generating a model that predicts the yield of the legume Vicia villosa Roth before harvest. In a previous study, satellite information from different dates throughout the complete phenological cycle of the legume cultivated in several plots of the Guaminí district (Buenos Aires) was used and a close relationship was found between yield level and the time series of vegetation indices. Based on these results, the possibility of predicting yield is evaluated considering nine dates of the 2021-2022 campaign between sowing and prior to harvest. They are associated in different ways, determining their impact on the accuracy of the trained model.
Results show that legume yield can be adequately classifed with remote monitoring of five dates. To have a prediction model could help in in-situ decisions by optimizing the use given to the crop (direct grazing, forage or seed production) based on its expected profit.
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Copyright (c) 2025 Fabián Marini, María Belén D'Amico, Guillermo Luis Calandrini, Juan Pablo Renzi, Guillermo Rubén Chantre
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