基于轻量化卷积神经网络改进模型的小麦赤霉病检测与评估方法

张旺; 任怡; 郭子迪; 李寒; 张漫; 刘洁; 仇瑞承

doi:10.1631/jzus.B2500225

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基于轻量化卷积神经网络改进模型的小麦赤霉病检测与评估方法

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Research Article | Updated：2026-05-15

基于轻量化卷积神经网络改进模型的小麦赤霉病检测与评估方法
Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2026年27卷第5期页码：450-465
Affiliations：

1.Key Laboratory of Smart Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
2.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
3.Yantai Academy of Agricultural Sciences, Yantai 265500, China
Funds：

the National Key Research and Development Program of China(2022YFD200150100-2022YFD200150101);the 2115 Talent Development Program of China Agricultural University
DOI：10.1631/jzus.B2500225
中图分类号：
CSTR：
收稿：2025-04-30，

修回：2025-09-06，

网络首发：2026-01-06，

纸质出版：2026-05-15
Accepted：

张旺,任怡,郭子迪等.基于轻量化卷积神经网络改进模型的小麦赤霉病检测与评估方法[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):450-465.

Wang ZHANG, Yi REN, Zidi GUO, et al. Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 450-465.
张旺,任怡,郭子迪等.基于轻量化卷积神经网络改进模型的小麦赤霉病检测与评估方法[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):450-465. DOI： 10.1631/jzus.B2500225.

Wang ZHANG, Yi REN, Zidi GUO, et al. Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 450-465. DOI： 10.1631/jzus.B2500225.

摘要

赤霉病作为小麦的一种常见病害，可导致小麦严重减产，并使籽粒产生霉菌毒素。选育抗赤霉病的小麦品种是减少病害损失的重要手段，但赤霉病的人工评估耗时费力且检测效率低。本研究提出了一种基于彩色图像和深度学习的小麦赤霉病检测与评估方法，可实现小麦赤霉病的快速检测。首先，本研究基于YOLO v8s架构设计了轻量化卷积神经网络模型，用于彩色图像中的麦穗检测，对麦穗检测平均精度达到0.964；随后基于麦穗检测结果，进一步开发用于小麦小穗与赤霉病检测的轻量化模型，通过引入space-to-depth卷积模块和BiFormer注意力模块，提高了对小目标群体的检测能力，结果显示该模型对小麦小穗和赤霉病的检测平均精度达到0.936；最后，基于小麦小穗的检测结果计算赤霉病病穗率与病情指数，评估小麦赤霉病的病害程度。本方法的病穗率和病情指数检测结果与真实值间的决定系数分别是0.71和0.93，因此，本方法能够实现对小麦赤霉病的精准和高效检测，有助于田间小麦赤霉病的定量评估。

Abstract

Fusarium head blight (FHB)

a frequent disease in wheat cultivation

can lead to substantial yield losses and the production of mycotoxins in grains. Therefore

the development of wheat varieties resistant to FHB is an important strategy to reduce related losses. In this respect

manual surveys of FHB are time-consuming and labor-intensive. To overcome this issue

this paper proposes a method for detecting and evaluating wheat FHB using color imaging and deep learning. Initially

a lightweight convolutional neural network model based on the You Only Look Once (YOLO) v8s artificial intelligence (AI) model was designed to detect wheat spikes from color images. Testing revealed that the model’s mean average precision in spike detection reached 0.964. Moreover

another lightweight model was developed for detecting wheat spikelet and FHB. To enhance the detection capability of the model for small objects

space-to-depth convolution (SPD-Conv) and BiFormer attention modules were integrated. The results indicated that the model can accurately detect spikelet and FHB

with a mean average precision of 0.936. Finally

based on the wheat spikelet detection results

the rate of diseased wheat spikes (RD_S) and the disease index for wheat (DI_W) were calculated to evaluate the severity of wheat FHB. For RD_S and DI_W

the coefficients of determination between phytologists’ evaluations and the estimates derived from the proposed method were 0.71 and 0.93

respectively. These results demonstrate that the proposed method facilitates the accurate and efficient detection of wheat FHB and contributes to the quantitative evaluation of FHB in the field.

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references

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