SPVD-DETR: A novel real-time end-to-end object detector of sweetpotato virus disease from unmanned aerial vehicle ortho imagery

Abstract

Sweetpotato is a crucial food crop globally, valued for both its economic significance and health benefits. However, the prevalence of sweetpotato virus diseases (SPVD) poses a serious threat to the industry, leading to reduced yields and economic losses for farmers. Efficient diagnostic techniques are essential for ensuring food security and consumer health. Traditional diagnostic methods are effective but suffer from complexity, time consumption, and high costs. To address these challenges, a novel real-time end-to-end detector called SPVD-DETR based on the Transformer architecture is proposed in this study. By utilizing the unmanned aerial vehicle (UAV) orthomosaic image, SPVD can be diagnosed in real-time at the field scale. First, aerial survey tasks are customized with automated drone tools to rapidly scan sweetpotato fields, generating high-resolution orthophotos and a stitched orthomosaic image for analysis. Then, the object detector is enhanced by incorporating efficient backbones and hybrid encoder modules such as cascaded group self-attention, attention-based scale fusion, and dynamic upsampling. Extensive ablation studies and comparative results show that SPVD-DETR achieves a good balance between real-time performance and accuracy. Next, the model is fine-tuned on the SPVD image tiles and achieves a detection accuracy of 31.3% mean average precision (mAP) with the fastest inference speed of 90 frames per second (FPS). Finally, the prediction results are mapped back to the orthomosaic image, estimating an overall SPVD incidence rate of 15% with a misdiagnosis rate of 14%. This study introduces a novel paradigm for detecting SPVD at the field scale, promoting automatic and intelligent plant disease detection for large-scale high-throughput phenotyping in precision agriculture.

Plant phenotyping relevance

UAV画像からサツマイモウイルス病の発生・罹病状態を推定する検出手法を開発し、アブレーション、比較評価、精度・速度・誤診率を検証しており、植物表現型取得が中心である。

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