Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Abstract

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Plant phenotyping relevance

小麦穂の画像検出を目的とするWheatNetを開発し、異なる生育段階・ドローン画像で精度評価しており、植物表現型取得手法が中心である。

Code and data availability

The paper's UAV wheat spike images and annotations are not deposited in any public repository; the Data Availability statement only says data are within the article/supplementary materials. The referenced GitHub URLs (roLabelImg, YOLOv5) are generic third-party tools, not authors' paper-specific assets.

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