Machine vision for precision agriculture has attracted considerable research interest in
recent years. The aim of this paper is to review the most recent work in the application of …

Weeds are one of the most harmful agricultural pests that have a significant impact on crops.
Weeds are responsible for higher production costs due to crop waste and have a significant …

Reducing the use of pesticides through selective spraying is an important component
towards a more sustainable computer-assisted agriculture. Weed identification at early …

Weeding is an effective way to increase crop yields. Reliable and accurate weed recognition
is a prerequisite for achieving high-precision site-specific weed control in precision …

In this paper we address the problem of recognising the Broad-leaved dock (Rumex
obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the …

Weed control is one of the biggest challenges in organic farms or nature reserve areas
where mass spraying is prohibited. Recent advancements in remote sensing and airborne …

Weed detection and classification are considered one of the most vital tools in identifying
and recognizing plants in agricultural fields. Recently, machine learning techniques have …

Convolutional Neural networks have endeavored to solve various problems in different
fields such as industries, medication, automation, etc. Among these areas, automatic farming …

Computer vision can lead toward more sustainable agricultural production by enabling
robotic precision agriculture. Vision‐equipped robots are being deployed in the fields to take …

Weeding is beneficial to the growth of the crops in field. At present, weeding in China mainly
relies on chemical herbicide spraying on a large area, which leads to environmental …