Detection and Analysis of Sow Targets Based on Image Vision

Kaidong Lei, Chao Zong, Ting Yang, Shanshan Peng, Pengfei Zhu, Hao Wang, Guanghui Teng and Xiaodong Du
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Kaidong Lei: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Chao Zong: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Ting Yang: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Shanshan Peng: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Pengfei Zhu: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Hao Wang: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Guanghui Teng: College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing 100083, China
Xiaodong Du: New Hope Liuhe Co., Ltd., Beijing 100102, China

Agriculture, 2022, vol. 12, issue 1, 1-19

Abstract: In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.

Keywords: computer vision; sow; image processing; behavior; precision livestock; animal welfare (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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