Transactions of the ASAE · 2003 · 107 citations · 22 references
AgriculturePlant SciencesWeed ScienceComputer VisionWeed EcologyWeed IdentificationCrop ClassificationWeed DetectionWeed ManagementWeed Spatial DistributionComputational EfficiencyComputational Efficiency AnalysisImage-based Weed ClassificationImage-based Weed DetectionImage-based Weed IdentificationImage-based Weed RecognitionMachine Vision Weed RecognitionReal-time Weed Recognition
A texturebased weed classification method was developed. The method consisted of a lowlevel Gaborwaveletsbased feature extraction algorithm and a highlevel neural networkbased pattern recognition algorithm. Thisclassification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grasscategories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur,velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied.After processing 40 sample images with 20 samples from each class, the results showed that the method was capable ofclassifying all the samples correctly with high computational efficiency, demonstrating its potential for practicalimplementation under realtime constraints.
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