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 …

Human community are educated about the environmental issues of pesticides and fertilizers
used in agriculture. There is a ever-growing demand for food to be met by agriculture …

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 …

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

Weed identification is fundamental toward developing a deep learning-based weed control
system. Deep learning algorithms assist to build a weed detection model by using weed and …

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 …

Weed identification in vegetable plantation is more challenging than crop weed identification
due to their random plant spacing. So far, little work has been found on identifying weeds in …

Weeds are aggressive, computing for light, water, nutrients and space for crops, garden
plants or lawn grass. Management of weeds usually consists of spraying herbicides in the …

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 …

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 …