Improved Crop and Weed Detection with Diverse Data Ensemble Learning in Agriculture

arXiv:2310.01055v2 Announce Type: replace-cross
Abstract: Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field, which can be achieved using deep learning techniques. In this regard, crop and weed-specific binary segmentation models have shown promise. However, uncontrolled field conditions limit their performance from one field to the other. To improve semantic model generalization, existing methods augment and synthesize agricultural data to account for uncontrolled field conditions. However, given …

abstract agriculture arxiv binary crops cs.ai cs.cv cs.lg data deep learning deep learning techniques detection diverse ensemble however localization management modern practices quantification regard segmentation type weeds