LWCD-YOLO: A Lightweight Corn Seed Kernel Fast Detection Algorithm Based on YOLOv11n

Wenbin Sun, Kang Xu, Dongquan Chen, Danyang Lv, Ranbing Yang (), Songmei Yang, Rong Wang, Ling Wang and Lu Chen
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Wenbin Sun: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Kang Xu: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Dongquan Chen: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Danyang Lv: Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
Ranbing Yang: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Songmei Yang: Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
Rong Wang: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Ling Wang: Haikou Experimental Station, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
Lu Chen: Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China

Agriculture, 2025, vol. 15, issue 18, 1-25

Abstract: As one of the world’s most important staple crops providing food, feed, and industrial raw materials, corn requires precise kernel detection for seed phenotype analysis and seed quality examination. In order to achieve precise and rapid detection of corn seeds, this study proposes a lightweight corn seed kernel rapid detection model based on YOLOv11n (LWCD-YOLO). Firstly, a lightweight backbone feature extraction module is designed based on Partial Convolution (PConv) and an efficient multi-scale attention module (EMA), which reduces model complexity while maintaining model detection performance. Secondly, a cross layer multi-scale feature fusion module (MSFFM) is proposed to facilitate deep feature fusion of low-, medium-, and high-level features. Finally, we optimized the model using the WIOU bounding box loss function. Experiments were conducted on the collected Corn seed kernel detection dataset, and LWCD-YOLO only required 1.27 million (M) parameters and 3.5 G of FLOPs. Its precision (P), mean Average Precision at 0.50 (mAP 0.50 ), and mean Average Precision at 0.50:0.95 (mAP 0.50:0.95 ) reached 99.978%, 99.491%, and 99.262%, respectively. Compared to the original YOLOv11n, the model size, parameter count, and computational complexity were reduced by 50%, 51%, and 44%, respectively, and the FPS was improved by 94%. The detection performance, model complexity, and detection efficiency of LWCD-YOLO are superior to current mainstream object detection models, making it suitable for fast and precise detection of corn seeds. It can provide guarantees for achieving seed phenotype analysis and seed quality examination.

Keywords: YOLOv11; object detection; deep learning; lightweight model; attention mechanism (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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