Weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that some of more prolific weeds are becoming resistant to the regular herbicides that are used and therefore more powerful and more expensive op...

Unmanned aerial systems (UAS) are able to deliver images of agricultural fields of high spatial and temporal resolution. It is, however, not trivial to extract quantitative information about weed infestations from images. This study contributes to weed research by using...

Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site‐specific weed management (SSWM) have become popular but require methods...

<p>Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using con...

Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two ...

International audience; In Precision Agriculture, remote sensors such as imaging systems are commonly used to manage site-specific weeds. Although there are several algorithms using spatial information, few tests proving their efficiency have been performed on relevant ...