A Smart Innovative Pre-Trained Model-Based QDM for Weed Detection in Soybean Fields
Abstract
Precision farming that takes advantage of the internet of things infrastructure now includes weed identification as a core component. Weeds now account for 45 percent of crop losses in farming because of competition with crops. This figure can be lowered with effective weed detecting technology. One of the most important areas of AI, known as deep learning (DL), is revolutionizing weed discovery for site-specific weed management (SSWM). In the past half a decade, DL methods have been used with both ground- and air-based technology for weed documentation in still images and in real time. According to the latest findings in DL-based weed detection, developing methods that aid precision weeding technologies in making informed decisions is a priority. Over the past five years, deep learning algorithms have been successfully incorporated into both ground-based and aerial-based systems for the purpose of weed identification in both still picture and real-time scenarios.