Using regression trees to predict citrus load balancing accuracy and costs

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.

Keywords

• citrus production
• regression trees
• machine learning
• feature selection