Estimation of beef cow body condition score: a machine learning approach using three-dimensional image data and a simple approach with heart girth measurements

Kojima, Tomoki; Oishi, Kazato; Aoki, Naoto; Matsubara, Yasushi; UETE, Toshiki; Fukushima, Yoshihiko; Inoue, Goichi; Sato, Say; SHIRAISHI, Toru; Hirooka, Hiroyuki; Masuda, Tatsuaki

doi:10.1016/j.livsci.2021.104816

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Prior to this study, imagining technology and depth cameras in particular have been utilized to accurately estimate BCS in dairy cows ( Liu et al, 2020 ). Kojima et al (2022) reported a model to estimate BCS of Wagyu beef cattle using depth imagery that yielded an accurate estimation; however, 62% of their cows were classified as a BCS 5 with 13% less than a 5 and 25% classified as a 6 or 7. Similarly in this study, while a skewed BCS dataset was also encountered, a higher testing accuracy of BCS categories of 70% was achieved.…”

Section: Discussionmentioning

confidence: 99%

Estimating body weight and body condition score of mature beef cows using depth images

Xiong

Condotta

Musgrave

et al. 2023

Translational Animal Science

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Obtaining accurate body weight (BW) is crucial for management decisions yet can be a challenge for cow-calf producers. Fast-evolving technologies such as depth sensing have been identified as low-cost sensors for agricultural applications but have not been widely validated for US beef cattle. This study aimed to 1) estimate the body volume of mature beef cows from depth images, 2) quantify BW and metabolic weight (MBW) from image-projected body volume, and 3) classify body condition scores (BCS) from image-obtained measurements using a machine-learning-based approach. Fifty-eight crossbred cows with a mean BW of 410.0 ± 60.3 kg and were between 4 and 6 years of age were used for data collection between May and December 2021. A low-cost, commercially available depth sensor was used to collect top-view depth images. Images were processed to obtain cattle biometric measurements, including MBW, body length, average height, maximum body width, dorsal area, and projected body volume. The dataset was partitioned into training and testing datasets using an 80/20% ratio. Using the training dataset, linear regression models were developed between image-projected body volume and BW measurements. Results were used to test BW predictions for the testing dataset. A machine-learning-based multivariate analysis was performed with 29 algorithms from eight classifiers to classify BCS using multiple inputs conveniently obtained from the cows and the depth images. A feature selection algorithm was performed to rank the relevance of each input to the BCS. Results demonstrated a strong positive correlation between the image-projected cow body volume and the measured BW (r = 0.9166). The regression between the cow body volume and the measured BW had a coefficient of determination (R 2) of 0.83 and a 19.2 ± 13.50 kg mean absolute error (MAE) of prediction. When applying the regression to the testing dataset, an increase in the MAE of the predicted BW (22.7 ± 13.44 kg) but a slightly improved R 2 (0.8661) was noted. Among all algorithms, the Bagged Tree model in the Ensemble class had the best performance and was used to classify BCS. Classification results demonstrate the model failed to predict any BCS lower than 4.5, while it accurately classified the BCS with a true prediction rate of 60, 63.6, and 50% for BCS between 4.75 and 5, 5.25 and 5.5, and 5.75 and 6, respectively. This study validated using depth imaging to accurately predict BW and classify BCS of US beef cow herds.

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Section: Discussionmentioning

confidence: 99%

Estimating body weight and body condition score of mature beef cows using depth images

Xiong

Condotta

Musgrave

et al. 2023

Translational Animal Science

View full text Add to dashboard Cite

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“…While the traditional body condition score (BCS) uses manual scoring, it is not encouraged because it is subjective, time-consuming, and stressful for the entire herd [91]. Recently, BCS estimation models based on image analysis and machine-learning techniques have been developed and used to estimate the body conditions of dairy cows [92]. As shown in Table 2, Huang et al ( 2019) captured the back-view images of cows using network cameras, manually labeled the key body parts such as tails, pins, and rumps in the images, and then used the single-shot multi-box detector (SSD) method to detect the tail and evaluate BCS.…”

Section: Assessment Of Body Conditionsmentioning

confidence: 99%

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

Liu,

Qi,

et al. 2023

Agriculture

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Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

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Accuracy of image analysis for linear zoometric measurements in dromedary camels

Gherissi

Lamraoui

Chacha

et al. 2022

Trop Anim Health Prod

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The present study consisted in verifying the effectiveness of the image analysis method for body measurement in dromedary camel compared to manual measurements as a reference method. To do this, twenty-one linear body measurements were estimated on 59 adult Sahraoui dromedary camels (22 males and 37 females) with normal clinical condition by the standard method using a measuring stick or vernier caliper. Image analysis on pro le, front or behind photographs were processed using Axiovision Software. Overall; mean comparison, relative error, variance, Pearson's correlation coe cient and coe cient of variance showed that the image analysis method is accurate in relation to the manual measurement. Furthermore, image analysis results indicated relevant accuracy (bias correction factor, Cb ≈1) and precision (Pearson ρ ≈ 1) which were signi cantly correlated with the results of the reference method (Lin's concordance correlation coe cients rccc ≈ 1). According to Blant Altman upper and lower limits of agreement, the concordance was estimated between 93.22 and 98.3%. Passing-Bablok regression showed good relationship between results of the two methods displaying no signi cant systematic and proportional bias. The image analysis method for linear body measurements in dromedary camel yielded results that are in agreement with the manual measuring method. This method is a valid tool for studies on camel conformation traits.

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Estimation of beef cow body condition score: a machine learning approach using three-dimensional image data and a simple approach with heart girth measurements

Cited by 6 publications

References 28 publications

Estimating body weight and body condition score of mature beef cows using depth images

Estimating body weight and body condition score of mature beef cows using depth images

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

Accuracy of image analysis for linear zoometric measurements in dromedary camels

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