Tassel Counting of Individual Ridge from Uav Rgb Imagery Based on Yolov8m with Deep Sort and Double-Step Otsu Thresholding Algorithm by Filtering Abnormal Ids for Maize Breeding

Abstract

Accurate maize tassel counting on individual ridge plays a critical role in advancing maize breeding programs by providing insights into crop adaptability and agronomic performance. An automated system with UAV-based RGB imagery is presented for maize tassel counting. The object detection model was developed based on You Only Look Once version 8-medium (YOLOv8m), which detects tassels. A double-step Otsu threshold algorithm (DSOTSUTA) was designed to extract individual maize tassel ridge, which eliminated the interference of two adjacent ridges on both sides. Individual ridge tassel counting was implemented by Deep learning based Simple Online and Realtime Tracking (Deep SORT). It assigned identifiers (IDs) to each tassel and filtered abnormal IDs by analyzing the displacement increments of IDs in consecutive frames eliminating errors caused by ID switching. The object detection model achieved a mean Average Precision (mAP) of 91.6%. The DSOTSUTA was tested on 5340 images and effectively extracted individual maize tassel ridges. The system achieved a root mean square error (RMSE) of 22.14 tassels per video, a mean absolute percentage error (MAPE) of 8.43%, and an accuracy of 92.23%, signifying strong agreement between the predicted tassel counts and ground truth observations. These results indicate that this automated system has the ability to enhance the accuracy of individual maize tassel counting in breeding programs.

Keywords: YOLOv8m, Maize tassels counting, Individual ridge, Double-step Otsu thresholding, Filter abnormal IDs