Detection of Maize Pathogenic Fungal Spores Based on Deep Learning

Yijie Ren, Ying Xu, Huilin Tian, Qian Zhang, Mingxiu Yang, Rongsheng Zhu, Dawei Xin, Qingshan Chen, Qiaorong Wei () and Shuang Song ()
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Yijie Ren: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Ying Xu: College of Plant Protection, Northeast Agricultural University, Harbin 150030, China
Huilin Tian: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Qian Zhang: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Mingxiu Yang: College of Plant Protection, Northeast Agricultural University, Harbin 150030, China
Rongsheng Zhu: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Dawei Xin: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Qingshan Chen: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Qiaorong Wei: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Shuang Song: State Key Laboratory of Smart Farm Technologies and Systems, College of Agriculture, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2025, vol. 15, issue 15, 1-18

Abstract: Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field.

Keywords: spore recognition; maize; small target detection; YOLOv8s; SPD-Conv (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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