Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Abstract

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R 2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Plant phenotyping relevance

小麦穂の画像検出・追跡による計数手法を開発し、精度・相関・リアルタイム性能を評価しており、植物形質（穂数）の取得が研究の中心である。

Code and data availability

The paper describes a wheat ear image/video dataset (233 annotated UAV images, 1631 augmented images, 20 test videos) and the YOLOv7xSPD Counter model, but no public repository, deposit, or authors' URL for the data or code is provided. The data availability statement only offers the material via inquiry to the authors

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