Weeds are unwanted plants that grow among crops. These weeds can significantly reduce the yield and quality of the farm output. Unfortunately, site-specific weed management is not followed in most of the cases. This paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images. The ability to selectively apply herbicides through the use of weed mapping and variable rate sprayers offers the potential to reduce harmful effects on the environment as well as to optimize producer profitability
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