Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach

descriptionPublicationkeyboard_double_arrow_right Article 01 Apr 2005 Spain English Publisher:Springer Science and Business Media LLCJournal:Precision Agriculture, volume 6, pages 213-228 (issn: 1385-2256, eissn: 1573-1618,

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Authors: Díaz, Beatriz; Ribeiro Seijas, Ángela; Bueno, Ricardo; Guinea Díaz, Domingo; Barroso, Judit; Ruiz, David; Fernández-Quintanilla, César;

doi: 10.1007/s11119-005-1036-1

handle: 10261/45604

Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach

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Abstract

In crop fields, weed density varies spatially in non-random patterns. Initial knowledge of weed distribution would greatly improve weed management for Precision Agriculture operations. Site properties could be correlated to weed distribution, since the former vary among crop fields and also certain factors such as soil texture or nitrogen may condition the weed growth. This paper presents a method, based on artificial intelligence techniques, for inducing a model that appropriately predicts the heterogeneous distribution of wild-oat (Avena sterilis L.) in terms of some environmental variables. From several experiments, distinct rule sets have been found by applying a genetic algorithm to carry out the automatic learning process. The best rule set extracted was able to explain about 88% of weed variability.

CICYT-AGF 1999–1125-C03–03 and MCYT- AGL2002-04468-C03-01.

16 páginas, ilustraciones y tablas estadísticas.

Peer reviewed

Country

Spain

Related Organizations

Spanish National Research Council
Spain

Keywords

Artificial intelligence, rules, Machine learning, Genetic algorithms, Data mining, Weed density

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