Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Since the number of people worldwide is anticipated to reach 9 billion people by 2050, the agriculture production needs to be increased up to 70% to manage the anticipated increasing of human demand. However, weeds are one of the most harmful factors that negatively impact the crops production, quality, and cause economical loses. Accordingly, automating the weed detection, classification, and counting of weeds per their growth stages will help farmers to choose the appropriate weeds’ controlling techniques. In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages. Additionally, a deep learning model (YOLOv5) was developed and trained for detecting weed, classifying weed’s growth stages, and counting the number of weeds occurrences in each part of the field. The results report that the best precision (82.7%) is generated by the Yolov5-Large model in detecting and classifying the weed’s growth stages. According to the best performance in terms of recall, Yolov5-sma11 model has the best recall of 79.4%. For counting the instances of weeds per the four growth stages in real-time, Yolov5-sma11 model showes counting time of 0.033 millisecond per frame.