Published Paper Details:

Weeds are one of the most important factors affecting agricultural production. Farmers often experience a bad agricultural yield as a result of weeds. It is becoming more and more clear that full-coverage chemical pesticide spraying pollutes and wastes farming ecosystems. Accurately identifying crops from weeds and obtaining precise spraying exclusively for weeds are crucial given the ongoing increase in agricultural production levels. With the continuous improvement in the agricultural production level, accurately distinguishing weeds from crops and achieving precise spraying only for weeds is important. However, precise spraying depends on accurately identifying and locating weeds and crops. Scholars have employed a variety of techniques to do this in recent years. In this project YOLO (You Only Look Once) v5 model was used to train the data set of the crop images along with the weed. The data set consists of images along with the labeled data of the weed. After the training was finished and the model was formed, we used StreamLit to establish a website where users could post pictures and videos to help identify weeds.