Weed Identification in Agricultural Fields Using Machine Learning Techniques

Abstract

Weeds compete with crops for water, nutrients, and sunshine, which is one of the most detrimental restraints on crop development. They also constitute a danger to agricultural output. The loss of worldwide productivity due to weeds and pests is likely to rise over the next few years. Using herbicide spray particularly in the field where the weeds are present is an efficient technique to manage the problem. For the weed control system to be properly deployed, weeds must be accurately and precisely detected. Traditional weed management techniques, however, take a long time and a lot of human resources, and they may have an adverse effect on the environment. To overcome this a model called Automatic weed management, a potential remedy that makes use of deep learning and machine learning approaches, has emerged to deal with these issues. This method increases agricultural productivity and reduces herbicides.