Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat

Wang ZHANG; Yi REN; Zidi GUO; Han LI; Man ZHANG; Jie LIU; Ruicheng QIU

doi:10.1631/jzus.B2500225

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Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat

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Research Article | Updated：2026-05-15

Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat
Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat
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
Journal of Zhejiang University-SCIENCE B Vol. 27, Issue 5, Pages: 450-465(2026)
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：
DOI：10.1631/jzus.B2500225
CLC：
CSTR：
Received：30 April 2025，

Revised：2025-09-06，

Online First：06 January 2026，

Published：15 May 2026
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

Abdu AM , Mokji MM , Sheikh UU , 2020 . Automatic vegetable disease identification approach using individual lesion features . Comput Electron Agric , 176 : 105660 . https://doi.org/10.1016/j.compag.2020.105660 https://doi.org/10.1016/j.compag.2020.105660

Agarwal M , Gupta SK , Biswas KK , 2020 . Development of efficient CNN model for tomato crop disease identification . Sustain Comput Infor Syst , 28 : 100407 . https://doi.org/10.1016/j.suscom.2020.100407 https://doi.org/10.1016/j.suscom.2020.100407

Ahlgren P , Kekäläinen J , 2007 . Indexing strategies for Swedish full text retrieval under different user scenarios . Inform Process Manag , 43 : 81 - 102 . https://doi.org/10.1016/j.ipm.2006.03.003 https://doi.org/10.1016/j.ipm.2006.03.003

Almoujahed MB , Rangarajan AK , Whetton RL , et al. , 2022 . Detection of Fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning . Comput Electron Agric , 203 : 107456 . https://doi.org/10.1016/j.compag.2022.107456 https://doi.org/10.1016/j.compag.2022.107456

AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine) , 2011 . Rules for monitoring and forecast of the wheat head blight , GB 15796-2011. National Standards of People’s Republic of China .

Basavaiah J , Arlene Anthony A , 2020 . Tomato leaf disease classification using multiple feature extraction techniques . Wireless Pers Commun , 115 : 633 - 651 . https://doi.org/10.1007/s11277-020-07590-x https://doi.org/10.1007/s11277-020-07590-x

Chen JD , Zhang DF , Zeb A , et al. , 2021 . Identification of rice plant diseases using lightweight attention networks . Expert Syst Appl , 169 : 114514 . https://doi.org/10.1016/j.eswa.2020.114514 https://doi.org/10.1016/j.eswa.2020.114514

Chen JR , Kao SH , He H , et al. , 2023 . Run, don’t walk: chasing higher FLOPS for faster neural networks . IEEE/CVF Conference on Computer Vision and Pattern Recognition , p. 12021 - 12031 . https://doi.org/10.1109/CVPR52729.2023.01157 https://doi.org/10.1109/CVPR52729.2023.01157

Chen YY , Wang XC , Zhang XL , et al. , 2023 . Spectral quantitative analysis and research of Fusarium head blight infection degree in wheat canopy visible areas . Agronomy , 13 ( 3 ): 933 . https://doi.org/10.3390/agronomy13030933 https://doi.org/10.3390/agronomy13030933

Chen ZR , Zhang YJ , Wang YQ , et al. , 2023 . Robust image inpainting forensics by using an attention-based feature pyramid network . Appl Sci , 13 ( 16 ): 9196 . https://doi.org/10.3390/app13169196 https://doi.org/10.3390/app13169196

Fang T , Chen P , Zhang J , et al. , 2020 . Crop leaf disease grade identification based on an improved convolutional neural network . J Electron Imag , 29 ( 1 ): 013004 . https://doi.org/10.1117/1.JEI.29.1.013004 https://doi.org/10.1117/1.JEI.29.1.013004

Ferentinos KP , 2018 . Deep learning models for plant disease detection and diagnosis . Comput Electron Agric , 145 : 311 - 318 . https://doi.org/10.1016/j.compag.2018.01.009 https://doi.org/10.1016/j.compag.2018.01.009

Figueroa M , Hammond-Kosack KE , Solomon PS , 2018 . A review of wheat diseases—a field perspective . Mol Plant Pathol , 19 ( 6 ): 1523 - 1536 . https://doi.org/10.1111/mpp.12618 https://doi.org/10.1111/mpp.12618

Gao CF , Guo W , Yang CH , et al. , 2024 . A fast and lightweight detection model for wheat Fusarium head blight spikes in natural environments . Comput Electron Agric , 216 : 108484 . https://doi.org/10.1016/j.compag.2023.108484 https://doi.org/10.1016/j.compag.2023.108484

Hellemans T , Landschoot S , Dewitte K , et al. , 2018 . Impact of crop husbandry practices and environmental conditions on wheat composition and quality: a review . J Agric Food Chem , 66 ( 11 ): 2491 - 2509 . https://doi.org/10.1021/acs.jafc.7b05450 https://doi.org/10.1021/acs.jafc.7b05450

Hong QQ , Jiang L , Zhang ZH , et al. , 2022 . A lightweight model for wheat ear Fusarium head blight detection based on RGB images . Remote Sens , 14 ( 14 ): 3481 . https://doi.org/10.3390/rs14143481 https://doi.org/10.3390/rs14143481

Howard A , Sandler M , Chen B , et al. , 2019 . Searching for MobileNetV3 . IEEE/CVF International Conference on Computer Vision , p. 1314 - 1324 . https://doi.org/10.1109/ICCV.2019.00140 https://doi.org/10.1109/ICCV.2019.00140

Hu J , Xie LH , Gu XA , et al. , 2022 . Information-interaction feature pyramid networks for object detection . IEEE 34th International Conference on Tools with Artificial Intelligence , p. 1301 - 1306 . https://doi.org/10.1109/ICTAI56018.2022.00197 https://doi.org/10.1109/ICTAI56018.2022.00197

Khan MS , Uandai SB , Srinivasan H , 2019 . Anthracnose disease diagnosis by image processing, support vector machine and correlation with pigments . J Plant Pathol , 101 ( 3 ): 749 - 751 . https://doi.org/10.1007/s42161-019-00268-9 https://doi.org/10.1007/s42161-019-00268-9

Liu GL , Reda FA , Shih KJ , et al. , 2018 . Image inpainting for irregular holes using partial convolutions . Proceedings of 15th European Conference on Computer Vision‒ECCV 2018, p. 89 - 105 . https://doi.org/10.1007/978-3-030-01252-6_6 https://doi.org/10.1007/978-3-030-01252-6_6

Liu ST , Huang D , Wang YH , 2019 . Learning spatial fusion for single-shot object detection . arXiv :1911. 09516 . https://doi.org/10.48550/arXiv.1911.09516 https://doi.org/10.48550/arXiv.1911.09516

Liu T , Zhao YY , Sun Y , et al. , 2024 . High-throughput identification of Fusarium head blight resistance in wheat varieties using field robot-assisted imaging and deep learning techniques . J Clean Prod , 480 : 144024 . https://doi.org/10.1016/j.jclepro.2024.144024 https://doi.org/10.1016/j.jclepro.2024.144024

Mao R , Wang ZC , Li FL , et al. , 2023 . GSEYOLOX-s: an improved lightweight network for identifying the severity of wheat Fusarium head blight . Agronomy , 13 : 242 . https://doi.org/10.3390/agronomy13010242 https://doi.org/10.3390/agronomy13010242

Mehta S , Rastegari M , 2021 . MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer . arXiv :2110. 02178 . https://doi.org/10.48550/arXiv.2110.02178 https://doi.org/10.48550/arXiv.2110.02178

Mustafa G , Zheng HB , Khan IH , et al. , 2024 . Enhancing Fusarium head blight detection in wheat crops using hyperspectral indices and machine learning classifiers . Comput Electron Agric , 218 : 108663 . https://doi.org/10.1016/j.compag.2024.108663 https://doi.org/10.1016/j.compag.2024.108663

Ouyang DL , He S , Zhang GZ , et al. , 2023 . Efficient multi-scale attention module with cross-spatial learning . ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing , p. 1 - 5 . https://doi.org/10.1109/ICASSP49357.2023.10096516 https://doi.org/10.1109/ICASSP49357.2023.10096516

Pantazi XE , Moshou D , Tamouridou AA , 2019 . Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers . Comput Electron Agric , 156 : 96 - 104 . https://doi.org/10.1016/j.compag.2018.11.005 https://doi.org/10.1016/j.compag.2018.11.005

Park J , Kim J , Kim CS , 2023 . BiFormer: learning bilateral motion estimation via bilateral transformer for 4K video frame interpolation . IEEE/CVF Conference on Computer Vision and Pattern Recognition , p. 1568 - 1577 . https://doi.org/10.1109/CVPR52729.2023.00157 https://doi.org/10.1109/CVPR52729.2023.00157

Qiu RC , Yang C , Moghimi A , et al. , 2019 . Detection of Fusarium head blight in wheat using a deep neural network and color imaging . Remote Sens , 11 ( 22 ): 2658 . https://doi.org/10.3390/rs11222658 https://doi.org/10.3390/rs11222658

Redmon J , Divvala S , Girshick R , et al. , 2016 . You only look once: unified, real-time object detection . IEEE Conference on Computer Vision and Pattern Recognition , p. 779 - 788 . https://doi.org/10.1109/CVPR.2016.91 https://doi.org/10.1109/CVPR.2016.91

Rößle D , Prey L , Ramgraber L , et al. , 2023 . Efficient noninvasive FHB estimation using RGB images from a novel multiyear, multirater dataset . Plant Phenomics , 5 : 0068 . https://doi.org/10.34133/plantphenomics.0068 https://doi.org/10.34133/plantphenomics.0068

Saccon FAM , Parcey D , Paliwal J , et al. , 2017 . Assessment of Fusarium and deoxynivalenol using optical methods . Food Bioprocess Tech , 10 : 34 - 50 . https://doi.org/10.1007/s11947-016-1788-9 https://doi.org/10.1007/s11947-016-1788-9

Sampathkumar S , Rajeswari R , 2022 . An automated crop and plant disease identification scheme using Cognitive Fuzzy C-Means algorithm . IETE J Res , 68 ( 5 ): 3786 - 3797 . https://doi.org/10.1080/03772063.2020.1780163 https://doi.org/10.1080/03772063.2020.1780163

Su WH , Zhang JJ , Yang C , et al. , 2021 . Automatic evaluation of wheat resistance to Fusarium head blight using dual mask-RCNN deep learning frameworks in computer vision . Remote Sens , 13 : 26 . https://doi.org/10.3390/rs13010026 https://doi.org/10.3390/rs13010026

Sunkara R , Luo T , 2022 . No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects . arXiv :2208. 03641 . https://doi.org/10.48550/arXiv.2208.03641 https://doi.org/10.48550/arXiv.2208.03641

Tan MX , Le QV , 2019 . EfficientNet: rethinking model scaling for convolutional neural networks . arXiv :1905. 11946 . https://doi.org/10.48550/arXiv.1905.11946 https://doi.org/10.48550/arXiv.1905.11946

Vincke D , Eylenbosch D , Jacquemin G , et al. , 2023 . Near infrared hyperspectral imaging method to assess Fusarium head blight infection on winter wheat ears . Microchem J , 191 : 108812 . https://doi.org/10.1016/j.microc.2023.108812 https://doi.org/10.1016/j.microc.2023.108812

Wang GQ , Chen MS , Lin YC , et al. , 2024 . Efficient multi-branch dynamic fusion network for super-resolution of industrial component image . Displays , 82 : 102633 . https://doi.org/10.1016/j.displa.2023.102633 https://doi.org/10.1016/j.displa.2023.102633

Wang YH , Li JJ , Su WH , 2023 . An integrated multi-model fusion system for automatically diagnosing the severity of wheat Fusarium head blight . Agriculture , 13 ( 7 ): 1381 . https://doi.org/10.3390/agriculture13071381 https://doi.org/10.3390/agriculture13071381

Zhang DY , Luo HS , Wang DY , et al. , 2022 . Assessment of the levels of damage caused by Fusarium head blight in wheat using an improved YoloV5 method . Comput Electron Agric , 198 : 107086 . https://doi.org/10.1016/j.compag.2022.107086 https://doi.org/10.1016/j.compag.2022.107086

Zhang DY , Luo HS , Cheng T , et al. , 2023 . Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network . Comput Electron Agric , 211 : 107968 . https://doi.org/10.1016/j.compag.2023.107968 https://doi.org/10.1016/j.compag.2023.107968

Zhao YS , Liu L , Xie CJ , et al. , 2020 . An effective automatic system deployed in agricultural internet of things using Multi-Context Fusion Network towards crop disease recognition in the wild . App Soft Comput , 89 : 106128 . https://doi.org/10.1016/j.asoc.2020.106128 https://doi.org/10.1016/j.asoc.2020.106128

Zheng L , Shen LY , Tian L , et al. , 2015 . Scalable person re-identification: a benchmark . IEEE International Conference on Computer Vision , p. 1116 - 1124 . https://doi.org/10.1109/ICCV.2015.133 https://doi.org/10.1109/ICCV.2015.133

Zhu L , Wang XJ , Ke ZH , et al. , 2023 . BiFormer: vision transformer with bi-level routing attention . IEEE/CVF Conference on Computer Vision and Pattern Recognition , p. 10323 - 10333 . https://doi.org/10.1109/CVPR52729.2023.00995 https://doi.org/10.1109/CVPR52729.2023.00995

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