SCS-YOLO: A real-time detection model for agricultural diseases — A case study of wheat fusarium head blight

Abstract

Fusarium head blight (FHB), which is triggered by fusarium graminearum, drastically reduces wheat yield and quality levels while generating harmful mycotoxins, compromising food security and the health of humans and livestock. Effective real-time detection of wheat FHB in field scenarios remains a critical challenge. Consequently, we present SCS-YOLO, an innovative real-time agricultural disease detection model for wheat FHB detection and severity assessment. Moreover, we successfully deployed it on the low-cost, low-power NVIDIA Jetson Nano embedded platform, achieving low-resource real-time detection. First, we restructured the YOLOv5s backbone network using StarNet, maintaining computational efficiency while obtaining richer and more expressive feature representations. Then, we proposed a novel lightweight CB module to replace the C3 module, further reducing the number of model parameters and the computational scale. Finally, we incorporated a weighted Shape-NWD function, which considers the shapes and sizes of bounding boxes, effectively improving the ability of the model to detect small objects. The results demonstrated that the SCS-YOLO model attained a mean average precision (mAP) of 90.51 % while reducing model parameters and giga floating-point operations (GFLOPs) by 39.97 % and 42.13 %, respectively, outperforming the existing models. Subsequently, the diseased spike rate was calculated, and its coefficient of determination (R²) and root mean square error (RMSE) were 0.90 and 3.48, respectively, effectively quantifying the severity of wheat FHB. Additionally, with an average inference time of only 0.26 s on NVIDIA Jetson Nano, SCS-YOLO exhibited strong potential for rapid detection of wheat FHB on edge devices. In summary, this study offers a dependable, efficient, and accurate solution for wheat FHB detection and assessment. Moreover, SCS-YOLO is designed to be flexible, enabling potential extension to the analysis of other crop diseases or crop types.

Plant phenotyping relevance

コムギ赤かび病の画像検出モデルを開発し、病穂率による病勢を定量評価しており、植物の病害状態を取得・推定する方法が研究の中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.