BACKGROUND In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to
the occurrence of weeds in close proximity with the crop. The objective of this research was …

Controlling weed infestation through chemicals (herbicides & pesticides) is essential for crop
yield. However, excessive use of these chemicals has caused severe agronomic and …

In order to overcome this threat imposed by weeds in agriculture, a measure is taken to
identify the weeds that grow along with the seedlings with the help of deep learning (DL) …

In modern agriculture, herbicides are most commonly used to control weeds. A large amount
of herbicide usage not only has an adverse effect on the soil but also has a severe …

Weed invasions pose a threat to agricultural productivity. Weed recognition and detection
play an important role in controlling weeds. The challenging problem of weed detection is …

Deep learning methods for weed detection typically focus on distinguishing weed species,
but a variety of weed species with comparable plant morphological characteristics may be …

The accurate identification of weeds in peanut fields can significantly reduce the use of
herbicides in the weed control process. To address the identification difficulties caused by …

Unmanned Aerial Vehicles (UAVs) have become an integral part of several real-world
applications. Their combination with other evolving paradigms such as image recognition …

Agricultural production is affected by many factors; in which weed is one of them to reduce
the production by soaking nutrition of the original plants. There are several traditional …

Weeds are among the major threats to cotton production. Overreliance on herbicides for
weed control has accelerated the evolution of herbicide-resistance in weeds and caused …