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A CNN-based methodology for cow heat analysis from endoscopic images

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In cattle farming, the artificial insemination technique is a biotechnology that brings to farmers a wide range of benefits namely health security, genetic gain and economic costs. The main condition for the success of artificial insemination within cattle is the heat (or estrus) detection. In this context, several cow heat detection systems have been recently proposed in the literature to assist the farmer in this task. Nevertheless, they are mainly based on the analysis of the physical behavior of the cow which may be affected by several factors related to its health and its environment. In this paper, we present a new vision system for cow heat detection which is based on the analysis of the genital tract of the cow. The main core of our system is a CNN model that has been designed and tailored for analyzing endoscopic images collected using an innovative insemination technology named Eye breed. The conducted experiments on two datasets namely our own dataset and a public dataset show the high accuracy of our CNN model (more than 97% for both datasets) outperforming 19 methods from the state of the art. Moreover, we propose an optimized version of our model for an Android deployment by exploiting several techniques namely quantization, GPU acceleration and video downsampling. The conducted tests on a smart-phone shows that our heat detection system has a response time of a few seconds.

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The authors gratefully acknowledge Claude Grenier, CEO Gènes Diffusion, Pierrick Drevillon CEO CECNA, Olivier Darasse, CEO Elexinn for the availability of data and the labeling of the videos.


This project has been funded by the FEDER European program, JUNIA French Engineering school and Gènes Diffusion French company.

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Correspondence to Ruiwen He.

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He, R., Benhabiles, H., Windal, F. et al. A CNN-based methodology for cow heat analysis from endoscopic images. Appl Intell 52, 8372–8385 (2022).

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