Systems to Monitor the Individual Feeding and Drinking Behaviors of Growing Pigs Based on Machine Vision

Yanrong Zhuang, Kang Zhou, Zhenyu Zhou, Hengyi Ji and Guanghui Teng ()
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Yanrong Zhuang: College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
Kang Zhou: College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
Zhenyu Zhou: College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
Hengyi Ji: College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
Guanghui Teng: College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2022, vol. 13, issue 1, 1-13

Abstract: Feeding and drinking behaviors are important in pig breeding. Although many methods have been developed to monitor them, most are too expensive for pig research, and some vision-based methods have not been integrated into equipment or systems. In this study, two systems were designed to monitor pigs’ feeding and drinking behaviors, which could reduce the impact of the image background. Moreover, three convolutional neural network (CNN) algorithms, VGG19, Xception, and MobileNetV2, were used to build recognition models for feeding and drinking behaviors. The models trained by MobileNetV2 had the best performance, with the recall rate higher than 97% in recognizing pigs, and low mean square error (RMSE) and mean absolute error (MAE) in estimating feeding (RMSE = 0.58 s, MAE = 0.21 s) and drinking durations (RMSE = 0.60 s, MAE = 0.12 s). In addition, the two best models trained by MobileNetV2 were combined with the LabVIEW software development platform, and a new software to monitor the feeding and drinking behaviors of pigs was built that can automatically recognize pigs and estimate their feeding and drinking durations. The system designed in this study can be applied to behavioral recognition in pig production.

Keywords: feeding behavior; drinking behavior; CNN; MobileNetV2; LabVIEW (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
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