A Phenotyping Perception Mechanism of Fusing Spatial and Channel Reconstruction Convolution Employing Maize-Breeding UAV Visual Images

Abstract

As precision agriculture advances, UAV-based aerial image object detection has emerged as a pivotal technology for maize-phenotyping perception operations. Complex backgrounds reduce the model’s performance in extracting features of maize tassels, while sacrificing model computation complexity to improve feature expression is detrimental to deployment on UAVs. To achieve a balance between the model size and deploy ability, an enhanced model incorporating spatial-channel convolution is proposed. First, a maize-breeding UAV was built, and the collection of maize tassel image data was realized. Second, Spatial and Channel Reconstruction Convolution (SCConv) was integrated into the neck network of the YOLOv8 baseline model, reducing the model computation complexity while maintaining the detection accuracy. Finally, the constructed maize tassel dataset and public Maize Tasseling Stage (MTS) dataset were used for the training and evaluation of the enhanced model. The results showed that the enhanced model achieved a precision of 92.2%, recall of 84.3%, and mAP@0.5 of 91.7%, with 7.3 G floating-point operations (FLOPs) and a model size of 5.16 MB. Compared with the original model, the enhanced model exhibited respective increases of 3.2%, 3.4%, and 3.4% in precision, recall, and mAP@0.5, along with respective reductions of 0.8 G FLOPs in computation complexity and 0.79 MB in model size. Compared with YOLOv10n, the precision, recall, and mAP@0.5 of the enhanced model are increased by 1.8%, 3.1%, and 2.9%, respectively, and the model computation is reduced by 0.3 G FLOPs, and the model size is reduced by 0.42 MB. The improved model is accurate, performs better on UAV aerial images in complex scenarios, and provides a methodological basis for deployment. It also supports maize tassel detection and holds potential for application in maize breeding.

Plant phenotyping relevance

トウモロコシ雄穂のUAV画像からの検出を目的に、軽量化したYOLOv8ベースの画像解析手法を開発・評価しており、植物形質取得が研究の中心である。

Code and data availability

The paper's own maize tassel UAV dataset (500 images) has no public availability statement; the only public URL mentioned is the MTS dataset, which is cited prior work [44], not a paper-specific asset. No author code, models, or data deposits are provided.

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