Open AccessMachine learning applied to the prediction of citrus production
Author(s) -
Irene Dı́az,
Silvia Matilde Mazza,
Elías F. Combarro,
Lucas Gabriel Giménez,
José E. Gaiad
Publication year - 2017
Publication title -
spanish journal of agricultural research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 36
eISSN - 2171-9292
pISSN - 1695-971X
DOI - 10.5424/sjar/2017152-9090
Subject(s) - biplot , mathematics , production (economics) , statistics , principal component analysis , orange (colour) , prime (order theory) , agriculture , agricultural engineering , computer science , horticulture , geography , engineering , biochemistry , chemistry , combinatorics , biology , gene , genotype , economics , macroeconomics , archaeology
An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.