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 …

Automated weeding is an important research area in agrorobotics. Weeds can be removed
mechanically or with the precise usage of herbicides. Deep Learning techniques achieved …

Agriculture is the backbone of our country. To support the increasing human population by
2050, the agricultural production must be doubled. The factors that affect the growth of the …

Weed management is of crucial importance in precision agriculture to improve productivity
and reduce herbicide pollution. In this regard, showing promising results, deep learning …

In recent years, edge computing and deep learning have been successfully performed
processing and classification tasks in a variety of fields including agriculture. Therefore, this …

Selective weed treatment is a cost-effective method that reduces manpower and usage of
the agrochemical, at the same time it requires an effective computer vision system to identify …

Weeds are among the major factors adversely affecting crop yield. Therefore, weed control
with minimal environmental damage is a global concern. Traditional weed control methods …

Research is being extensively conducted on using deep learning in the field of crop and
weed segmentation based on images captured with a camera. However, the segmentation …

The presence of weeds in agricultural crops has been one of the problems of greatest
interest in recent years as they consume natural resources and negatively affect the …

In the context of precision agriculture, the effective distinction between crops and weeds is
the key to weeding. In recent years, various fields such as deep learning and machine vision …