Agriculture is a fundamental source of nourishment for humanity on this planet. In the present day, as a result of the increasing population, there is a need for agriculture to have a higher level of productivity in order to fulfill the needs. In ancient times, people used traditional techniques to enhance output, such as using cow dung as a natural fertilizer in agricultural areas. This led to a significant boost in output that was sufficient to fulfill the population's demands. However, subsequent individuals contemplated the idea of maximizing earnings by increasing the yield. Thus, a revolt known as the "Green Revolution" emerged. Subsequently, the use of lethal toxins as herbicides has escalated significantly over this time frame. While our efforts to increase production have been successful, we have overlooked the detrimental impact on the environment, which raises concerns about our ability to sustain ourselves on this magnificent planet. In this effort, we have used techniques to minimize herbicide consumption by selectively spraying them only in regions where weeds are present. This study presents the implementation of image processing techniques using MATLAB to identify and locate regions of weed growth in a picture captured from agricultural fields. With the global population expanding and available land and natural resources diminishing, experts are paying greater attention to precision agriculture. Image processing methodologies may be used to address this issue. Keywords: Image processing, Agriculture, Color segmentation .