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 …

Background Weed recognition is key for automatic weeding, which is a challenging problem.
Weed recognition is mainly based on different features of crop images. The extracted image …

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 …

The success of weed control is critical for our food security. Non-chemical weed control is a
promising technique in sustainable agriculture to ensure the food security. In this review …

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 …

Weed control in pasture is a challenging problem that can be expensive and
environmentally unfriendly. This paper proposes a novel method for recognition of broad …

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in
grassland that needs removal. It affects dairy products and reduces their quality. Hand …

The main purpose of the work is to evaluate the deep machine learning algorithms used for
the distinction between weeds and crop plants using the open database of images of the …

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf
stereo camera sensor data using deep learning holds several benefits for farmers, from …

Highlights•Crop/weed classification algorithm is proposed using a BoVW based on SIFT or
SURF.•The highest classification accuracy obtained was 96.5% with zero false …