Plant Detection and Counting using Yolo based Technique

In agricultural applications, identifying and counting plants in plot photos is essential for yield estimation, crop monitoring, and resource optimization. The YOLO (You Only Look Once) method is used in this work to properly identify and count plants in plot photos. The algorithm was trained using the Roboflow platform through a supervised learning procedure. This method provides an automated and effective solution for plant analysis in agriculture by utilizing the power of machine learning. A broad dataset of plot photos with plants is gathered as part of the technique, and each plant instance is then annotated with exact bounding boxes. The Roboflow platform is used to manage and annotate data effectively. For plant detection, the YOLO method, known for its real-time object detection capabilities, is used. YOLO achieves impressive detection speed without sacrificing accuracy by dividing the input image into a grid and forecasting bounding boxes and class probabilities for each grid cell. The suggested method shows promising outcomes in precisely identifying and counting plants in plot photos. By giving farmers, agronomists, and researchers access to insightful information for crop management and decision-making, it has the potential to greatly improve agricultural practices. The methodology can be improved in the future, and its application can be broadened to include more plant species and climatic situations.