Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

Abstract

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

“…Moreover, several papers recently appeared in the literature related to our works. Studies on feeding behaviors around calving in dairy cattle [22] and predicting the feed intake of cattle based on jaw movement using a triaxial accelerometer [23] are some of them. Although the approaches are different from ours, since feeding time estimation and feed intakes are related, further analysis would be worthwhile to investigate in more detail [24,25].…”

Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production

“…Cattle have historically been primarily evaluated on the basis of weight gain alone, as sensor technology to capture data at the individual level is a relatively recent development, and sensor uptake in the beef sector has lagged behind that of the dairy sector [ 26 , 27 , 28 ]. Models have previously been developed for cattle which predict DMI and residual feed intake [ 29 , 30 ], including models which utilise animal-mounted sensors [ 5 , 31 ]. Similar models have been developed for small ruminants [ 32 ].…”

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

“…In addition, Ding et al ( 2022) evaluated an integrated machine-learning algorithm framework to identify jaw movements during feeding using a triaxial accelerometer at a relatively low sampling frequency, and it was also used to predict feed intake on the basis of the acceleration variables of ingesting and chewing activities. The results showed that three feeding activities-ingesting, chewing, and ingesting-chewing-could be effectively identified using the XGB and Viterbi algorithms with a precision of 99% [13].…”

“…Research has demonstrated the possibility of combining data from image, sound, and movement sensors with algorithms for dairy farm decision-making [10][11][12]. Ding et al (2022) used a wearable device equipped with accelerometers to measure the feeding behavior of cattle, and fourteen machine-learning models were established and compared in order to predict feed intake rates [13]. Gardenier et al (2018) presented a perception system that is suitable for automatic lameness detection.…”