Journal of Zhejiang University SCIENCE  B

Accepted manuscript available online (unedited version)


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


Author(s):  Wang ZHANG1, 2, Yi REN1, 2, Zidi GUO1, 2, Han LI1, 2, Man ZHANG1, 2, Jie LIU3, Ruicheng QIU1, 2

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

Corresponding email(s):  Ruicheng QIU, qrc@cau.edu.cn Jie LIU, liujie@yt.shandong.cn

Key Words:  Fusarium head blight; Lightweight neural network; Disease detection; YOLO; Deep learning


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Wang ZHANG1,2, Yi REN1,2, Zidi GUO1,2, Han LI1,2, Man ZHANG1,2, Jie LIU3, Ruicheng QIU1,2. Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2500225

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journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2500225"
}

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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 YOLOv8s was designed to detect wheat spikes from color images. Testing showed that the mean average precision of the model in spike detection reached 0.964. Moreover, another lightweight model was developed for detecting wheat spikelets and FHB. To enhance the detection capability of the model for small objects, space-to-depth convolution 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 (DI_W) were calculated, in order 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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On-line Access: 2025-10-10

Received: 2025-04-30

Revision Accepted: 2025-09-06

Crosschecked: 0000-00-00

Cited: 0

Clicked: 1937

Citations:  Bibtex RefMan EndNote GB/T7714

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