A Unified Approach for Weed Detection in Arable Acreage Using RetinaNet Architecture

One of the main elements that could reduce agricultural productivity is weeds. With the evolution in Computer Vision technologies, Deep Learning integrated with image processing techniques has proven to be an effective tool for the detection of weed. This study analyzes the benefits of accurate weed detection, which can help farmers to reduce the use of pesticides and increase agricultural output. Also, this study reviews various techniques used in recent years and discusses about the datasets that are used to train these techniques and presents certain challenges faced by the existing weed detection techniques in various situations. Moreover, this research study emphasizes the possible application of drones or unmanned aircraft systems for capturing high-quality images of weeds in fields for enabling precise treatment of weed affected areas. This study employs the VGG-16 network based RetinaNet architecture to analyze RGB photos of weeds in agricultural fields, which can aid farmers in reducing pesticide usage and enhancing crop yields.