Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing productivity. The wastage pollution farmland's natural atmosphere instigated full coverage chemical herbicide spraying are increased. Since proper identification weeds from crops helps to reduce usage improve productivity, this study presents novel deep learning based detection classification (CVDL-WDC) model for agriculture. proposed CVDL-WDC technique intends properly discriminate plants as well weeds. involves two namely multiscale Faster RCNN object optimal extreme machine (ELM) classification. parameters ELM optimally adjusted farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis against benchmark dataset reported enhanced outcomes over its recent approaches interms several measures.