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Sensors
Volume 25
Issue 23
10.3390/s25237132
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Intelligent System for Pigeon Egg Management: Integrating a Novel Lightweight YOLO Model and Multi-Frame Fusion for Robust Detection and Positioning

by
Yufan Cheng
1,
Yao Liu
1,2,
Qianhui Li
1,
Tao Jiang
1,
Chengyue Ji
1,
Longshen Liu
1,
Ya Zhong
1,*,
Jinling Wu
1 and
Guanchi Chen
1
1
College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 211800, China
2
School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(23), 7132; https://doi.org/10.3390/s25237132
Submission received: 24 October 2025 / Revised: 18 November 2025 / Accepted: 19 November 2025 / Published: 21 November 2025
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions)
Versions Notes

Abstract

To address the issues of high breakage rates and substantial labor costs in pigeon egg farming, this study proposes an intelligent pigeon egg recognition and positioning system based on an improved YOLOv12n object detection algorithm and OpenCV barcode recognition technology. Visual sensors installed on feeding machines were used to collect real-time video data of pigeon cages, with images obtained through frame extraction. The images were annotated using LabelImg to construct a pigeon egg detection dataset containing 1500 training images, 215 validation images, and 215 test images. After data augmentation, the dataset was used to train the pigeon egg recognition model. Additionally, customized barcodes were designed according to actual farm conditions and recognized using OpenCV through preprocessing steps including grayscale conversion, filtering, and binarization to extract positional information. Experimental results demonstrate that the proposed YOLOv12n-pg recognition model requires only 4.9 GFLOPS computational load, contains 1.56 M parameters, and has a model size of 3.5 MB, significantly lower than other models in the YOLO-n series. In inference tests, it achieved 99.4% mAP50 and 83.6% mAP50-95. The implementation of a majority voting method in practical testing further reduced the missed detection rate. The system successfully records “cage location—egg count” information as key-value pairs in a database. This system effectively enables automated management of pigeon eggs, improves recognition performance, and demonstrates higher efficiency and accuracy compared to manual operations, thereby establishing a foundation for subsequent research in pigeon egg recognition.
Keywords: pigeon egg recognition; pigeon egg localization; target detection; image processing pigeon egg recognition; pigeon egg localization; target detection; image processing

Share and Cite

MDPI and ACS Style

Cheng, Y.; Liu, Y.; Li, Q.; Jiang, T.; Ji, C.; Liu, L.; Zhong, Y.; Wu, J.; Chen, G. An Intelligent System for Pigeon Egg Management: Integrating a Novel Lightweight YOLO Model and Multi-Frame Fusion for Robust Detection and Positioning. Sensors 2025, 25, 7132. https://doi.org/10.3390/s25237132

AMA Style

Cheng Y, Liu Y, Li Q, Jiang T, Ji C, Liu L, Zhong Y, Wu J, Chen G. An Intelligent System for Pigeon Egg Management: Integrating a Novel Lightweight YOLO Model and Multi-Frame Fusion for Robust Detection and Positioning. Sensors. 2025; 25(23):7132. https://doi.org/10.3390/s25237132

Chicago/Turabian Style

Cheng, Yufan, Yao Liu, Qianhui Li, Tao Jiang, Chengyue Ji, Longshen Liu, Ya Zhong, Jinling Wu, and Guanchi Chen. 2025. "An Intelligent System for Pigeon Egg Management: Integrating a Novel Lightweight YOLO Model and Multi-Frame Fusion for Robust Detection and Positioning" Sensors 25, no. 23: 7132. https://doi.org/10.3390/s25237132

APA Style

Cheng, Y., Liu, Y., Li, Q., Jiang, T., Ji, C., Liu, L., Zhong, Y., Wu, J., & Chen, G. (2025). An Intelligent System for Pigeon Egg Management: Integrating a Novel Lightweight YOLO Model and Multi-Frame Fusion for Robust Detection and Positioning. Sensors, 25(23), 7132. https://doi.org/10.3390/s25237132

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