A method for small-sized wheat seedlings detection: from annotation mode to model construction.

Abstract

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Plant phenotyping relevance

コムギ幼苗の検出・計数という植物個体数形質を対象に、アノテーション方式と深層学習モデルを開発・評価しており、表現型取得手法が研究の中心である。

Code and data availability

The paper's wheat seedling UAV image dataset (6000 augmented patches with local/global annotations) and the improved YOLOv5 model are paper-specific assets, but the article states they are only available from the corresponding author upon reasonable request; no public repository or authors' URL is provided. The GitHub/

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