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. The current manual method of processing male inflorescences has low accuracy and high labor intensity. Moreover, the existing algorithms cannot be directly applied to the counting of an individual ridge maize tassels. 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 a mean absolute percentage error (MAPE) of 8.43 % 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 ridge maize tassel counting in breeding programs.

Plant phenotyping relevance

UAV画像からトウモロコシ雄穂を自動検出・追跡・計数する手法を開発し、精度評価まで行っており、植物形質取得が研究の中心である。

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