2018 · 26 citations · 7 references
AgricultureComputer SciencePrecision AgricultureComputer VisionMachine VisionWeed IdentificationAgricultural Machine VisionCrop ClassificationObject RecognitionSupport Vector MachinesWeed DetectionWeed Infestation MonitoringFeature-based RepresentationImage-based Weed DetectionImage-based Weed IdentificationImage-based Weed RecognitionMachine Vision Weed IdentificationMachine Vision Weed RecognitionSoil Color IndicesSupport Vector Machine Classification
Precision Agriculture is a clear standout of applying the most recent advances of intelligent systems. The motivation behind the adoption of such a systems is to reduce costs, increment treatments quality and efficiency, thus, increasing the quantity and the quality of agricultural products. In our study we used a histograms based on color indices to discriminate between three classes: soil, soybean and broad-leaf(weeds). This feature representation was tested with two classifiers Back-propagation neural network (BPNN), and Support Vector Machine (SVM). Our approach achieved a state of the art performance with an overall accuracy of 96.601% for BPNN, and 95.078% SVM.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text