Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley

By R. Gerhards, S. Christensen. 2003

223 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/f074bffc074002d8d5df45a856eae612e02c32d8

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In Agronomy and Crop Science

In Plant Science

Citations in the corpus

Applications of Artificial Intelligence in Agriculture: A Review (2019, background): “Weed control in sugar-beet, maize, winter wheat, and winter barley, can be done by applying online weed detection using digital image analysis taken by an UAV (drone), computer‐based decision making and global positioning system (GPS)‐controlled patch spraying [67].”
Assessment of leaf cover and crop soil cover in weed harrowing research using digital images (2007, background): “There has been an increase in work on remote sensing by photoelectric sensors within site-specific weed management (Gerhards & Christensen, 2003; Scotford & Miller, 2005) but results in the context of weed harrowing are either negative or inconclusive when trying to establish a standard method…”
Field spectroscopy for weed detection in wheat and chickpea fields (2013, background): “Other ground-based, on-the-go sensing methods are designed to detect the shape of weed leaves against a light-toned soil background, and can thus be applied only in the early growing stages (Gerhards and Christensen 2003; Weis et al. 2008).”
Automated Crop and Weed Monitoring in Widely Spaced Cereals (2006, methodology): “Whilst the problem has attracted some research interest more recently ( Gerhards and Christensen 2003 ), a need still remains for a method which can be used under a wide range of field conditions.”
Ground-level hyperspectral imagery for detecting weeds in wheat fields (2013, background): “Site-specific weed management has reduced herbicide use by 11–90 % without affecting crop yield (Feyaerts and van Gool 2001; Gerhards and Christensen 2003).”