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Computer vision techniques have attracted a great interest in precisionagriculture, recently. The common goal of all computer vision-based precisionagriculture tasks is to detect the objects of interest (e.g., crop, weed) anddiscriminating them from the background. The Weeds are unwanted plants growingamong crops competing for nutrients, water, and sunlight, causing losses tocrop yields. Weed detection and mapping is critical for site-specific weedmanagement to reduce the cost of labor and impact of herbicides. This paperinvestigates the use of color and texture features for discrimination ofSoybean crops and weeds. Feature extraction methods including two color spaces(RGB, HSV), gray level Co-occurrence matrix (GLCM), and Local Binary Pattern(LBP) are used to train the Support Vector Machine (SVM) classifier. Theexperiment was carried out on image dataset of soybean crop, obtained from anunmanned aerial vehicle (UAV), which is publicly available. The results fromthe experiment showed that the highest accuracy (above 96%) was obtained fromthe combination of color and LBP features.

Supervised learning for crop/weed classification based on color and texture features