Vision-based measuring method for individual cow feed intake using depth images and a Siamese network

Xinjie Wang, Baisheng Dai, Xiaoli Wei, Weizheng Shen, Yonggen Zhang, Benhai Xiong

Abstract


Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows, which can also evaluate the utilization rate of pasture feed. To achieve an automatic and non-contact measurement of feed intake, this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images. An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24 150 samples. A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data. The experimental results show that the mean absolute error (MAE) and the root mean square error (RMSE) of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively, which outperformed existing works. This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.
Keywords: computer vision, Siamese network, cow feed intake, depth image, precision livestock farming
DOI: 10.25165/j.ijabe.20231603.7985

Citation: Wang X J, Dai B S, Wei X L, Shen W Z, Zhang Y G, Xiong B H. Vision-based measuring method for individual cow feed intake using depth images and a Siamese network. Int J Agric & Biol Eng, 2023; 16(3): 233–239.

Keywords


computer vision, Siamese network, cow feed intake, depth image, precision livestock farming

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References


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