Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns

Frequent checks on livestock\u2019s body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health\u2019s anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals\u2019 body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow\u2019s growth through a three-dimensional analysis of their bodies\u2019 portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect\u2122 v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coef\ufb01cients of determination R2> 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R2 = 0.74) and back slope (R2 = 0.12)

An evaluation study of 3D imaging technology as a tool to estimate body weight and growth in dairy heifers

The aim of this thesis was to evaluate the use of a 3D camera as a tool to estimate body weight and growth in dairy heifers. Data collection lasted from October 2022 to January 2023 and was performed at the Swedish Livestock Research Centre in Uppsala, Sweden. Data collection included a total of 165 dairy heifers of two breeds: 96 Swedish Red and 69 Swedish Holstein. Body weight, 3D images and a set of nine different body measurements: body length, chest girth, hip width, backside width, ischial width, hip ischial width, withers height, hip height and external width between the hip joints, were collected at six different data collection occasions. All heifers with a full set of manual body measurements and BWs from the scale (n=46) were used in the statistical analysis. Pearson correlations were used to investigate the relationship between each body measurement and body weight. The highest correlation was found between body weight and chest girth (r = 0.94). The correlation between the body weight and the external hip width (r = 0.91), hip ischial width (r = 0.82) and hip height (r = 0.79) were also among the highest. Body measurements with a correlation ≥ 0.75 (external hip width, hip ischial width, hip width, backside width, hip height, chest girth) were used in the model development together with Point cloud images collected by the 3D camera. Three models, based on data from the 46 heifers with a full data set, were created to predict body weight: 1) a regression model using the manual body measurements as input, 2) a regression model based on the manual body measurements together with the Point cloud image data, 3) a machine learning conventional neural network using the Point cloud image data as model input. The performance of the prediction models were assessed using R2 and root mean square error (RMSE). Model 1 showed the best performance among the three models (R2 = 0.81, RMSE = 17.04 kg). Combining the image data with the body measurements (Model 2) did not improve the model, in fact, lowered the R2 value (0.41) and increased RMSE (27.13 kg). Model 3 was slightly better than Model 2 with an R2 value of 0.53 (RMSE = 22.77 kg). Despite the small dataset, the results show potential in creating a model extracting the body measurements from the Point cloud image data rather than only using the point cloud image information. However, not possible to extract body features partly due to the distance between the camera and the heifer, especially for younger heifers with not yet pronounced body features. Several of the previously described and commonly used body measurements were shown to be useful in estimating body weight. Furthermore, the hip ischial width, not described previously, showed a considerably high correlation to body weight and could thus be used in future automatic feature extraction using 3D imaging technology

Precision technologies to address dairy cattle welfare: focus on lameness, mastitis and body condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and realtime assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.FE1B-06B2-126F | Jos? Pedro Pinto de Ara?joN/

Predicting Body Weight of Cattle and Nutrient Digestion of Individual Sweet Bran Components to Improve Beef Cattle Production Efficiency

Efficiency is of high importance to beef cattle producers to ensure that beef cattle are being raised without unnecessary resource use. Two separate experiments were conducted to evaluate potential strategies to improve efficiency in beef cattle production. A metabolism study was conducted to evaluate digestion parameters of individual Sweet Bran components. The objective was to determine the effects of diet components on nutrient digestion and rumen fermentation parameters. The individual components of Sweet Bran (Corn bran, solvent-extracted germ meal, and corn steep liquor) were included at 40% diet dry matter (DM) in a steam-flaked corn (SFC) based diet. A SFC-based finishing diet was also utilized as a control. Digestibility of OM was greatest for steep and SFC control treatments, while least for germ meal and bran treatments (P = 0.02) when using TiO2 as an external marker. Similarly, OM digestibility was greatest for steep, intermediate for SFC control, and least for germ meal and bran when using ADIA as an internal marker (P \u3c 0.01). Another study was conducted to determine the accuracy of a time-of flight depth camera for predicting body weight of beef heifers. Predicting body weight without the need of a walk-over scale presents opportunities to reduce labor needs and provide accurate body weight estimations more frequently than traditional systems. The objective was to determine the accuracy of using a time-of-flight depth camera as a method to predict shrunk BW of yearling beef heifers. Prediction of shrunk BW using image-extracted dorsal projected volume compared to shrunk BW measured using a walk-over scale produced an R2 = 0.89 and SEM = 3.29 kg. The accuracy of this study is comparable to other research using depth cameras, indicating the potential for application. Further research is needed to identify and improve markers for ruminal digesta flow and to further develop methods of predicting BW of beef cattle. Advisors: Yijie Xiong and Andrea Watso

Development and validation of a fully automated 2D imaging system generating body condition scores for dairy cows using machine learning

ABSTRACT: Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully automated 2-dimensional imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate datasets were used for training and testing. The training dataset included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits at 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing dataset was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice, allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS), resulting in a testing dataset of 9,657 single BCS. A total of 3,817 cows in the testing dataset were scored twice 30 d (±2 d) apart, and the change in their BCS (ΔBCS) was calculated. A subset of cows at one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the interobserver agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing dataset, intra- and interobserver agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels and the weighted kappa (κw) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablok regressions. The system had an almost perfect repeatability with a κw of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κw of 0.69. The overall PA at the exact, ± 0.25-unit, and ± 0.50-unit BCS error range between MAN_BCS and AI_BCS was 44.4%, 84.6%, and 94.8%, respectively, and greater than the PA obtained between HA1 and HA3. The Bland-Altman plot revealed a minimal systematic bias of −0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κw of 0.20, the overall PA at the exact and ± 0.25-unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers

Prediction of feed efficiency based on test-day liveweight of dairy cows estimated using animal characteristics and milk mid-infrared spectra

Due to the increasing world population, the consumption of milk and dairy products is raising. Optimizing the production of milk under the economic and environmental constraints is a challenge. In this context, knowing the feed efficiency (FE) of dairy cow is a key issue. Unfortunately, the acquisition of such records at individual and large scales is unfeasible. So, the current thesis aims to fill this gap by developing a FE predictive tool for dairy sector available at large and individual scales. To achieve this objective, the FE was assessed by calculating the ratio of fat and protein corrected milk (FPCM) to dry matter intake (DMI). Although FPCM is already routinely available, this is not the case for DMI. However, a literature equation exists to predict DMI from FCM, live bodyweight (BW) and the number of weeks of lactation. Recently, a methodology using the milk mid-infrared (MIR) spectrometry combined with the animal characteristics was developed to predict test-day BW records, leading to open new perspectives about the use of FE related traits for management and breeding purposes. So, the final achievement of this thesis is the development and the implementation of a FE prediction tool from traits easily recorded by dairy herd improvement (DHI) organizations, including namely the BW estimation. To achieve this objective, 3 main research activities were conducted in this thesis. The first research aimed to increase the calibration set and to apply feature selection algorithms during the modeling in order to improve the BW models' robustness and accuracy. Indeed, the presence of less informative variables in a prediction equation could impact negatively its robustness. Three feature selection algorithms were applied on 280 predictors to select the most informative ones from a dataset containing 5,920 records: partial least squares regression (PLS) combined with sum of ranking difference (SRD), PLS combined with uninformative variables elimination (UVE), and the output of Elastic net regression (EN). Parity, days in milk (DIM), milk yield (MY), and two MIR spectral points were selected as the most relevant variables to predict BW. Validation root mean square errors (RMSEp) of 60 kg were obtained for both PLS and EN regressions employing these 5 predictors, suggesting a better robustness of these models compared to the ones without MIR or using all 277 MIR variables. The RMSE values of validation set coming from another brand of spectrometer were around 64 kg. The second research work focused on the implementation of a BW equation in practice. Indeed, some poor quality BW predictions can be obtained using poor quality spectral data or by applying the model on samples for which the variability was not included in the calibration set. So, the objective of this work was to develop data cleaning methods easy to implement by DHI to ensure the quality of BW predictions. So, 3 data cleaning procedures and their combinations were tested on a DHI dataset containing 346,818 records: the deletion of 1% of extreme high and low predicted values (M1), the deletion of records when the global-H (GH) distance was greater than 5 (M2), and the deletion of records if the absolute fat residual value was higher than 0.30 g/dL of milk (M3). The interest of those procedures was assessed by estimating the root mean square differences (RMSD) between fat, protein, and fatty acids traits predicted by the MIR spectrometry internally and externally. All methods allowed to decrease RMSD, the gain ranged from 0.32% to 41.39%. Based on the obtained results, the “M1 and M2” combination should be preferred to be more parsimonious in the data loss as it had the higher ratio of RMSD gain to data loss. However, to ensure the lowest RMSD, the “M2 or M3” combination was the most relevant. Based on these 2 first works, FE records were easily obtained at large and individual scales and cleaned appropriately. Then, in order to assess the relevancy of the FE tool, the final work of this thesis consisted to study the behaviors of BW and FE predictions obtained from the 5 developed equations within and between lactations as well as per test month and compared them to the ones observed in the literature using reference values. Subsets of Hebei (N=288,607) and Walloon (N=379,472) DHI datasets were used. Even if the BW equations did not differ a lot based on the prediction performances, differences were observed on the DHI datasets and clustered the equations within 2 groups: the ones including 5 predictors and the one using the parity, DIM, MY, and the full MIR spectral data. The final one depicted a more expected evolution within lactation with a drop of BW around 30-50 days in milk and then an increase. This was not observed for the other equations suggesting a lack of MIR information to take into account sufficiently the individual variability of BW. The annual trend observed for this equation was also more expected with a BW drop during the grazing period. However, the differences in FE predictions obtained using BW estimated from different equations were less marked, suggesting a low sensibility of FE predictions to a moderate variation of BW. The validation RMSE reached 0.05 suggesting a good accuracy for this FE indicator. In conclusion, the FE tool developed in this thesis can be implemented by DHI organizations based on the BW equation including milk, parity, DIM, and the 277 MIR spectral points. However, some additional investigations are still needed before the use of a such tool by DHI. Indeed, even if preliminary results obtained in this thesis suggested a moderate heritability of FE trait, a study with the newly developed BW equations could be done as we have observed that the past BW equation tended to overestimate BW. Moreover, knowing the relationships between FE predictions with other traits having economic interest is required before any use. A reflection about the best way to communicate the results to the farmers must be also started. Finally, the FE tool was based on an equation predicting DMI. For the future, it could be also of interest to measure the relevancy of this equation by using DMI reference data

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

Publication history: Accepted - 7 December 2016; Published online - 1 February 2017.Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH4) emissions from individual cows. Several techniques have been developed to measure CH4 in a research setting but most are not suitable for large-scale recording on farm. Several groups have explored proxies (i.e., indicators or indirect traits) for CH4; ideally these should be accurate, inexpensive, and amenable to being recorded individually on a large scale. This review (1) systematically describes the biological basis of current potential CH4 proxies for dairy cattle; (2) assesses the accuracy and predictive power of single proxies and determines the added value of combining proxies; (3) provides a critical evaluation of the relative merit of the main proxies in terms of their simplicity, cost, accuracy, invasiveness, and throughput; and (4) discusses their suitability as selection traits. The proxies range from simple and low-cost measurements such as body weight and high-throughput milk mid-infrared spectroscopy (MIR) to more challenging measures such as rumen morphology, rumen metabolites, or microbiome profiling. Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely onfarm. Proxies related to body weight or milk yield and composition, on the other hand, are relatively simple, inexpensive, and high throughput, and are easier to implement in practice. In particular, milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, and combinations of 2 or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by 15 to 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). The most important applications of CH4 proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for traits of lower environmental impact, single or multiple proxies can be used as indirect criteria for the breeding objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate estimates of CH4, the greatest limitation today is the lack of robustness in their general applicability. Future efforts should therefore be directed toward developing combinations of proxies that are robust and applicable across diverse production systems and environments.Technical and financial support from the COST Action FA1302 of the European Union

Development and integration of animal-based welfare indicators, including pain, in goat farms in Portugal

Tese de Doutoramento em Ciências Veterinárias, especialidade de ClínicaThe development of species-specific protocols for on-farm welfare assessment represents a major concern for the European Union agricultural policy. Proper welfare assessment demands for the use of valid, reliable and feasible animal-based welfare indicators. The literature and policy-makers recognise the need for advancing knowledge in this area. This thesis aims at developing and integrating animal-based indicators in on-farm welfare assessment protocols, focusing on dairy goat farms. Specifically, this thesis contributes to literature in three areas. First, it contributes to the identification of animal-based welfare indicators that should be included in welfare assessment protocols. We conducted a literature review that allowed for the recognition of the need for future research in the indicators’ psychometric properties, such as reliability and feasibility. Secondly, this thesis develops tools to assist the measurement of body condition and lameness. For body condition, we developed a visual body condition scoring system (BSC). Our approach requires minimum animal handling without compromising a valid and reliable individual assessment of the goats. With respect to lameness we developed a websurvey that allowed us to collect observer’s ratings of goats lameness condition. Our survey showed that observers were only able to consistently assess severely lame goats, a finding which is important towards the integration of the indicator in assessment protocols. The observers’ ratings also showed that the numerical rating scales should only be used considering their ordinal level of measurement. This directs research towards the development of scoring systems with higher levels of measurement, like the modified visual analogue scales. Third, this thesis contributed to the development of a welfare assessment protocol that integrated and tested the two studied indicators (BCS and lameness). Such protocol was implemented in 30 Portuguese farms and provided insights into the main welfare problems affecting intensively kept dairy goats in our country (claw overgrowth, queuing at feeding, very fat animals), which is paramount to improve dairy goats’ welfare. Research conducted for this thesis has practical implications for both welfare assessment research and to the goat industry in general. Ultimately, through the development of adequate assessment tools, it integrates the welfare issue into the food chain, meeting the consumers’ expectations in the development of a sustainable food production system.RESUMO - Desenvolvimento e integração de indicadores de bem-estar animal, incluindo dor, em explorações de cabras em Portugal - A elaboração de protocolos de avaliação de bem-estar específicos para cada espécie pecuária é uma preocupação da política agrícola europeia. A literatura da área de bem-estar animal identifica a criação de instrumentos de medição como o primeiro passo para a elaboração destes protocolos. Esta tese tem como objetivo desenvolver e integrar indicadores para incluir em protocolos de avaliação para utilizar em explorações de cabras de aptidão leiteira. Esta tese apresenta três contributos para a literatura de bem-estar animal. Em primeiro lugar, contribui para a identificação de indicadores, baseados nos animais, com potencial para integração em protocolos de avaliação. A revisão bibliográfica realizada permitiu reconhecer a necessidade premente de investigação nesta área, dado que a maior parte dos indicadores necessitam de ser testados e validados. Em segundo lugar, esta tese desenvolve ferramentas para apoiar a avaliação da condição corporal e da claudicação. Para a condição corporal foi criado um sistema visual de avaliação considerado válido e repetível, e que apenas necessita de uma breve contenção dos animais para ser utilizado. Relativamente à claudicação foram recolhidas participações de observadores relativamente à observação de vídeos de cabras com diferentes níveis de claudicação. A análise destas observações permitiu concluir que os participantes apenas são consistentes a avaliar os casos mais graves de claudicação, facto importante para a integração do indicador em protocolos de avaliação. As classificações dos observadores mostraram ainda que as escalas numéricas em uso apenas podem ser utilizadas considerando um nível ordinal de medição. Este facto abre o caminho para o desenvolvimento de escalas com níveis mais elevados de medição, como as escalas visuais analógicas modificadas. Em terceiro lugar, esta tese desenvolve um protocolo de avaliação que inclui e testa os indicadores condição corporal e claudicação. Este protocolo permitiu investigar sobre os maiores problemas de bem-estar que afetam as explorações intensivas de leite de cabra em Portugal (sobre crescimento das unhas, filas na manjedoura, animais gordos), sendo esta informação fundamental para analisar como melhorar o bem-estar das cabras de leite. A investigação conduzida no âmbito desta tese apresenta implicações práticas tanto para o estudo do bem-estar animal, como para a exploração de leite de cabra. O desenvolvimento de ferramentas adequadas de avaliação permite a integração da valoração do bem-estar na cadeia de produção, indo ao encontro das expectativas dos consumidores para a concepção de sistemas mais sustentáveis de produção de alimentos

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management