Modelling Wild-Oat Density in Terms of Soil Factors:
A Machine Learning Approach
RICARDO BUENO AND
Industrial Automation Institute-CSIC, Ctra.Campo Real. Km 0.2, 28500 Madrid, Spain
DAVID RUIZ AND
CCMA-CSIC, Serrano 115 B, 28006 Madrid, Spain
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 dis-
tribution of wild-oat (Avena sterilis L.) in terms of some environmental variables. From several experi-
ments, 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.
Keywords: artiﬁcial intelligence, data mining, genetic algorithms, machine learning, rules, weed density
Weed infestations in crops are still a challenge that has to be met in agriculture.
Usually, weeds are heterogeneously distributed in agricultural ﬁelds (Cardina et al.,
1997). Thus, diﬀerent sampling procedures have been used to detect and describe the
spatial distribution of weeds within a ﬁeld (Rew and Cousens, 2001). However, weed
discrimination is often a diﬃcult task, particularly when weeds and crops have
similar morphological and/or spectral characteristics. In spite of this, the spatial
variability of weed abundance constitutes the basis for site speciﬁc weed manage-
ment systems. Using these systems, farmers could spray selectively to reduce the
amount of herbicide usage thereby diminishing environmental impact as well as
economic cost (Earl et al., 1996).
The persistence of high-density weed areas in ﬁelds over time suggests a non-
random distribution that probably depends on environmental variability in the ﬁeld.
Moreover, soil characteristics as well as the properties of plant species, have a strong
inﬂuence on the growth and reproduction of both crop and weed. Some studies
Precision Agriculture, 6, 213–228, 2005
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