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  4. Vol 11, Issue 1, 2026

Intelligent Poultry Health Recognition from Fecal Images Using YOLO11n

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DOI: 10.23977/jeis.2026.110110 | Downloads: 1 | Views: 102

Author(s)

Yajie Liu 1, Zhi Liu 1, Shuaichen Yuan 1, Yuqin Wang 1, Yiyi Long 1, Lu Gao 1

Affiliation(s)

1 School of Information Engineering, Henan Vocational University of Science and Technology, Zhoukou, China

Corresponding Author

Shuaichen Yuan

ABSTRACT

Poultry industry plays a vital role in the livestock industry. In poultry production, disease breakouts can cause a fall in production performance and, at times, can cause significant mortality which could result in tremendous economic loss. To realize the low-cost, contactless poultry health status detection for large-scale poultry houses, this study explores the method of detecting the four types of poultry health status according to the images of poultry feces. A total of 6812 photographs were taken on farms, of four types: Healthy, Coccidiosis, Newcastle Disease and Salmonellosis. The dataset has been divided into training, test and validation sets in a ratio of 7:2:1. In this study YOLO11n models were trained from scratch and with pretrained weights. The results show that the pretrained model achieved Precision, Recall, [email protected] and [email protected]:0.95 of 85.1%, 73.1%, 83.6%, and 63.1%, respectively; the model trained from scratch achieved 81.5%, 76.0%, 82.8%, and 59.9%, respectively. In addition, pretraining boosted the overall Precision by 3.6%, [email protected] by 0.8%, and [email protected]:0.95 by 3.2%. This procedure can be used as an automatic preliminary screening procedure for abnormal feces in chicken houses.

KEYWORDS

Poultry Healthy; Fecal Images; YOLO11n; Object Detection; Transfering Learning

CITE THIS PAPER

Yajie Liu, Zhi Liu, Shuaichen Yuan, Yuqin Wang, Yiyi Long, Lu Gao. Intelligent Poultry Health Recognition from Fecal Images Using YOLO11n. Journal of Electronics and Information Science (2026). Vol. 11, No. 1, 75-83. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2026.110110.

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