International Journal of Computational Intelligence Systems

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Volume 12, Issue 1, November 2018, Pages 79 - 89

Using regression trees to predict citrus load balancing accuracy and costs

Authors
G. R. R. Bóbeda1, griseldabobeda@gmail.com, E. F. Combarro2, efernandezca@uniovi.es, S. Mazza3, smmazza@gmail.com, L. I. Giménez4, laugim@yahoo.com, I. Díaz5, sirene@uniovi.es
1Faculty of Agronomy, Northeastern National University, Sargento Juan Bautista Cabral 2131, Corrientes, W3402, Argentina
2Computer Science Department, University of Oviedo, Computer Science Department, Jesús Arias de Velasco, Oviedo, Asturias 33005, Spain
3Faculty of Agronomy, Northeastern National University, Sargento Juan Bautista Cabral 2131, Corrientes, W3402, Argentina
4Faculty of Agronomy, Northeastern National University, Sargento Juan Bautista Cabral 2131, Corrientes, W3402, Argentina
5Computer Science Department, University of Oviedo, Computer Science Department, Jesús Arias de Velasco, Oviedo, Asturias 33005, Spain
Received 2 March 2018, Accepted 3 August 2018, Available Online 1 November 2018.
DOI
10.2991/ijcis.2018.25905183How to use a DOI?
Keywords
citrus production; regression trees; machine learning; feature selection
Abstract

In order to define management and marketing strategies, farmers need adequate knowledge about future yield with the greatest possible accuracy and anticipation. In citrus orchards, greater variability and non-normality of yield distributions complicate the early estimation of fruit production. This study was conducted with the objective of developing a method to estimate citrus load based on orchard characteristics, morphological information of trees and number of fruits in defined locations of the crow. Field data from 16 citrus orchards obtained from 2005/06 through 2013/14 seasons were used. Machine learning techniques were applied to predict yield; these methods can reduce the estimation error as well as decrease the need for in-field measuring, thus reducing both the cost and time of the process.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 1
Pages
79 - 89
Publication Date
2018/11/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2018.25905183How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - G. R. R. Bóbeda
AU  - E. F. Combarro
AU  - S. Mazza
AU  - L. I. Giménez
AU  - I. Díaz
PY  - 2018
DA  - 2018/11/01
TI  - Using regression trees to predict citrus load balancing accuracy and costs
JO  - International Journal of Computational Intelligence Systems
SP  - 79
EP  - 89
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905183
DO  - 10.2991/ijcis.2018.25905183
ID  - Bóbeda2018
ER  -