Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach |
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Authors: | Email author" target="_blank">Beatriz?DiazEmail author Angela?Ribeiro Ricardo?Bueno Domingo?Guinea Judit?Barroso David?Ruiz Cesar?Fernadez-Quintanilla |
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Institution: | (1) Industrial Automation Institute-CSIC, Ctra.Campo Real. Km 0.2, 28500 Madrid, Spain;(2) CCMA-CSIC, Serrano 115 B, 28006 Madrid, Spain |
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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. |
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Keywords: | artificial intelligence data mining genetic algorithms machine learning rules weed density |
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