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 …

Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

Weeds are a significant threat to agricultural productivity and the environment. The
increasing demand for sustainable weed control practices has driven innovative …

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

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 …

Weeds are a major threat to crops, making early detection critical for maintaining agricultural
productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt …

Weeds are one of the most important factors affecting agricultural production. The waste and
pollution of farmland ecological environment caused by full-coverage chemical herbicide …

Weeds can decrease yields and the quality of crops. Detection, localisation, and
classification of weeds in crops are crucial for developing efficient weed control and …

In modern agriculture, there are many technologies that improve the performance of farming
and production of the main plant. Few such important technologies are the machine learning …

Weeds are among the major factors that could harm crop yield. With the advances in
electronic and information technologies, machine vision combined with image processing …