A fast and lightweight detection model for wheat fusarium head blight spikes in natural environments

Abstract

Fusarium head blight (FHB) is one of the most common and destructive infections in wheat, posing a serious threat to food security and human health. Real-time detection and severity assessment of wheat FHB are crucial for effective management and loss assessment. In this study, we proposed a novel and advanced YOLOv5s model for fast and accurate detection of wheat FHB. Firstly, we replaced the original backbone with MobileNetV3 and integrated spatial pyramid pooling-fast (SPPF). Subsequently, we replaced the C3 modules with the C3Ghost to reduce the parameters and complexity of this model without compromising detection performance. The proposed model was trained and evaluated on the wheat FHB dataset, achieving a high mean average precision (mAP) of 97.15 % using only 3.64 M parameters and 4.77 G floating-point operations (FLOPs). Compared with the original YOLOv5, these values have decreased by 49.72 % and 71.32 %, respectively. To assess the severity of FHB damage, the diseased spike rate was calculated and the coefficient of determination (R²) was 0.9802, achieving a satisfactory statistical effect. These results validate the effectiveness of the improved YOLOv5s model for real-time detection of wheat FHB spikes. This study contributes to the development of accurate and efficient plant disease detection systems and provides a valuable reference for accurate quantitative assessment of crop losses, thus ensuring food security.

Plant phenotyping relevance

小麦の赤かび病穂を画像から検出し、罹病穂率によって病害重症度を定量化する深層学習手法の開発・評価が中心であるため、植物病害フェノタイピングに該当する。

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.