Early Estimation of Tomato Yield by Decision Tree Ensembles
Journal
Agriculture
ISSN
2077-0472
Date Issued
2022
Author(s)
Mario Lillo-Saavedra
Alberto Espinoza-Salgado
Angel García-Pedrero
Camilo Souto
Eduardo Holzapfel
Consuelo Gonzalo-Martín
Marcelo Somos-Valenzuela
Type
Resource Types::text::journal::journal article
URL Institutional Repository
Abstract
Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28%.
Cite this document
Lillo-Saavedra, M., Espinoza-Salgado, A., García-Pedrero, A., Souto, C., Holzapfel, E., Gonzalo-Martín, C., Somos-Valenzuela, M., & Rivera, D. (2022). Early estimation of tomato yield by decision tree ensembles. Agriculture, 12(10), 1655. https://doi.org/10.3390/agriculture12101655
Subjects
crop yield
;
decision tree ensemble
;
uavs