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

Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach 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. Precision Agriculture Springer Journals

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

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Kluwer Academic Publishers
Copyright © 2005 by Springer Science+Business Media, Inc.
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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