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Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Department of Mechanical Systems Engineering, Aichi University of Technology, Gamagori-shi, Aichi 443-0047, Japan
Animals 2020, 10(5), 771; https://doi.org/10.3390/ani10050771
Received: 12 March 2020 / Revised: 16 April 2020 / Accepted: 27 April 2020 / Published: 29 April 2020
(This article belongs to the Special Issue Animal-Centered Computing)
Direct observation of mammalian behavior requires a substantial amount of effort and time, particularly if the number of animals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, different machine learning methods were applied to detect and estimate whether a goat is in estrus, based on the goat’s behavior. The percentage concordance (PC) of their behavior, based on tracking data and human observations, was evaluated. The results establish that HMM is an adequate method from the viewpoints of estimation, statistical, and time series modeling. In this experiment, neural network did not seem to be adequate method, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.
Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation. View Full-Text
Keywords: shiba goat; Estrus; estimation; machine learning shiba goat; Estrus; estimation; machine learning
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Arakawa, T. Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods. Animals 2020, 10, 771.

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