May 14, 2024, 4:46 a.m. | Alireza Ghanbari, Gholamhassan Shirdel, Farhad Maleki

cs.CV updates on arXiv.org arxiv.org

arXiv:2405.07157v1 Announce Type: new
Abstract: Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impact. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders …

abstract advanced agriculture annotated data application arxiv automated cs.ai cs.cv data decision deep learning domain domain adaptation efficiency environmental environmental impact growth head however impact making precision productivity segmentation semi tasks technologies type visual waste while

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