2022
1.
Ahmad Alsahaf,; Radu Gheorghe,; André Hidalgo,; Nicolai Petkov,; Azzopardi, George
Pre-insemination prediction of dystocia in dairy cattle Journal Article
Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@article{Alsahaf2022,
title = {Pre-insemination prediction of dystocia in dairy cattle},
author = {Ahmad Alsahaf, and Radu Gheorghe, and Andr\'{e} Hidalgo, and Nicolai Petkov, and George Azzopardi
},
doi = {https://doi.org/10.1016/j.prevetmed.2022.105812},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
journal = {Preventive Veterinary Medicine},
volume = {210},
number = {105812},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {article}
}
2.
Bhole, Amey; Udmale, Sandeep S; Falzon, Owen; Azzopardi, George
CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection, convnets, deep learning, noise suppression, pattern recognition, smart farming
@article{bhole2022corf3d,
title = {CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images},
author = {Amey Bhole and Sandeep S Udmale and Owen Falzon and George Azzopardi},
doi = {https://doi.org/10.1016/j.eswa.2021.116354},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Expert Systems with Applications},
volume = {192},
number = {116354},
publisher = {Pergamon},
abstract = {Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push\textendashpull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of was obtained for the 5-fold cross validation and for the leave-one day-out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable.},
keywords = {brain-inspired, contour detection, convnets, deep learning, noise suppression, pattern recognition, smart farming},
pubstate = {published},
tppubtype = {article}
}
Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push–pull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of was obtained for the 5-fold cross validation and for the leave-one day-out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable.
2020
3.
Heide, EMM; Kamphuis, C; Veerkamp, RF; Athanasiadis, IN; Azzopardi, G; Pelt, ML; Ducro, BJ
Improving predictive performance on survival in dairy cattle using an ensemble learning approach Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: ensemble learning, predictive analysis, smart farming
@article{van2020improving,
title = {Improving predictive performance on survival in dairy cattle using an ensemble learning approach},
author = {EMM Heide and C Kamphuis and RF Veerkamp and IN Athanasiadis and G Azzopardi and ML Pelt and BJ Ducro},
doi = {https://doi.org/10.1016/j.compag.2020.105675},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {177},
number = {105675},
publisher = {Elsevier},
abstract = {Cow survival is a complex trait that combines traits like milk production, fertility, health and environmental factors such as farm management. This complexity makes survival difficult to predict accurately. This is probably the reason why few studies attempted to address this problem and no studies are published that use ensemble methods for this purpose. We explored if we could improve prediction of cow survival to second lactation, when predicted at five different moments in a cow’s life, by combining the predictions of multiple (weak) methods in an ensemble method. We tested four ensemble methods: majority voting rule, multiple logistic regression, random forest and naive Bayes. Precision, recall, balanced accuracy, area under the curve (AUC) and gains in proportion of surviving cows in a scenario where the best 50% were selected were used to evaluate the ensemble model performance. We also calculated correlations between the ensemble models and obtained McNemar’s test statistics. We compared the performance of the ensemble methods against those of the individual methods. We also tested if there was a difference in performance metrics when continuous (from 0 to 1) and binary (0 or 1) prediction outcomes were used. In general, using continuous prediction output resulted in higher performance metrics than binary ones. AUCs for models ranged from 0.561 to 0.731, with generally increasing performance at moments later in life. Precision, AUC and balanced accuracy values improved significantly for the naive Bayes and multiple logistic regression ensembles in at least one data set, although performance metrics did remain low overall. The multiple logistic regression ensemble method resulted in equal or better precision, AUC, balanced accuracy and proportion of animals surviving on all datasets and was significantly different from the other ensembles in three out of five moments. The random forest ensemble method resulted in the least significant improvement over the individual methods.},
keywords = {ensemble learning, predictive analysis, smart farming},
pubstate = {published},
tppubtype = {article}
}
Cow survival is a complex trait that combines traits like milk production, fertility, health and environmental factors such as farm management. This complexity makes survival difficult to predict accurately. This is probably the reason why few studies attempted to address this problem and no studies are published that use ensemble methods for this purpose. We explored if we could improve prediction of cow survival to second lactation, when predicted at five different moments in a cow’s life, by combining the predictions of multiple (weak) methods in an ensemble method. We tested four ensemble methods: majority voting rule, multiple logistic regression, random forest and naive Bayes. Precision, recall, balanced accuracy, area under the curve (AUC) and gains in proportion of surviving cows in a scenario where the best 50% were selected were used to evaluate the ensemble model performance. We also calculated correlations between the ensemble models and obtained McNemar’s test statistics. We compared the performance of the ensemble methods against those of the individual methods. We also tested if there was a difference in performance metrics when continuous (from 0 to 1) and binary (0 or 1) prediction outcomes were used. In general, using continuous prediction output resulted in higher performance metrics than binary ones. AUCs for models ranged from 0.561 to 0.731, with generally increasing performance at moments later in life. Precision, AUC and balanced accuracy values improved significantly for the naive Bayes and multiple logistic regression ensembles in at least one data set, although performance metrics did remain low overall. The multiple logistic regression ensemble method resulted in equal or better precision, AUC, balanced accuracy and proportion of animals surviving on all datasets and was significantly different from the other ensembles in three out of five moments. The random forest ensemble method resulted in the least significant improvement over the individual methods.
2019
4.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Hanenberg, Egiel; Veerkamp, Roel F; Petkov, Nicolai
Estimation of Muscle Scores of Live Pigs Using a Kinect Camera Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: image processing, pattern recognition, predictive analysis, smart farming
@article{alsahaf2019estimation,
title = {Estimation of Muscle Scores of Live Pigs Using a Kinect Camera},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Egiel Hanenberg and Roel F Veerkamp and Nicolai Petkov},
doi = {10.1109/ACCESS.2019.2910986},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {IEEE Access},
volume = {7},
pages = {52238--52245},
publisher = {IEEE},
abstract = {The muscle grading of livestock is a primary component of valuation in the meat industry. In pigs, the muscularity of a live animal is traditionally estimated by visual and tactile inspection from an experienced assessor. In addition to being a time-consuming process, scoring of this kind suffers from inconsistencies inherent to the subjectivity of human assessment. On the other hand, accurate, computer-driven methods for carcass composition estimation, such as magnetic resonance imaging (MRI) and computed tomography scans (CT-scans), are expensive and cumbersome to both the animals and their handlers. In this paper, we propose a method that is fast, inexpensive, and non-invasive for estimating the muscularity of live pigs, using RGB-D computer vision and machine learning. We used morphological features extracted from the depth images of pigs to train a classifier that estimates the muscle scores that are likely to be given by a human assessor. The depth images were obtained from a Kinect v1 camera which was placed over an aisle through which the pigs passed freely. The data came from 3246 pigs, each having 20 depth images, and a muscle score from 1 to 7 (reduced later to 5 scores) assigned by an experienced assessor. The classification based on morphological features of the pig's body shape-using a gradient boosted classifier-resulted in a mean absolute error of 0.65 in tenfold cross-validation. Notably, the majority of the errors corresponded to pigs being classified as having muscle scores adjacent to the groundtruth labels given by the assessor. According to the end users of this application, the proposed approach could be used to replace expert assessors at the farm.},
keywords = {image processing, pattern recognition, predictive analysis, smart farming},
pubstate = {published},
tppubtype = {article}
}
The muscle grading of livestock is a primary component of valuation in the meat industry. In pigs, the muscularity of a live animal is traditionally estimated by visual and tactile inspection from an experienced assessor. In addition to being a time-consuming process, scoring of this kind suffers from inconsistencies inherent to the subjectivity of human assessment. On the other hand, accurate, computer-driven methods for carcass composition estimation, such as magnetic resonance imaging (MRI) and computed tomography scans (CT-scans), are expensive and cumbersome to both the animals and their handlers. In this paper, we propose a method that is fast, inexpensive, and non-invasive for estimating the muscularity of live pigs, using RGB-D computer vision and machine learning. We used morphological features extracted from the depth images of pigs to train a classifier that estimates the muscle scores that are likely to be given by a human assessor. The depth images were obtained from a Kinect v1 camera which was placed over an aisle through which the pigs passed freely. The data came from 3246 pigs, each having 20 depth images, and a muscle score from 1 to 7 (reduced later to 5 scores) assigned by an experienced assessor. The classification based on morphological features of the pig's body shape-using a gradient boosted classifier-resulted in a mean absolute error of 0.65 in tenfold cross-validation. Notably, the majority of the errors corresponded to pigs being classified as having muscle scores adjacent to the groundtruth labels given by the assessor. According to the end users of this application, the proposed approach could be used to replace expert assessors at the farm.
2018
5.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Hanenberg, Egiel; Veerkamp, Roel F; Petkov, Nicolai
Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@article{alsahaf2018prediction,
title = {Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Egiel Hanenberg and Roel F Veerkamp and Nicolai Petkov},
doi = {https://doi.org/10.1093/jas/sky359},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Journal of animal science},
volume = {96},
number = {12},
pages = {4935--4943},
publisher = {Oxford University Press US},
abstract = {The weight of a pig and the rate of its growth are key elements in pig production. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In this paper, we used machine learning, namely random forest (RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values (EBVs), along with pedigree and pedigree-genetic relationships. Moreover, we presented a 2-step data reduction procedure, based on random projections (RPs) and principal component analysis (PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R2 = 0.62). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately.},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {article}
}
The weight of a pig and the rate of its growth are key elements in pig production. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In this paper, we used machine learning, namely random forest (RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values (EBVs), along with pedigree and pedigree-genetic relationships. Moreover, we presented a 2-step data reduction procedure, based on random projections (RPs) and principal component analysis (PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R2 = 0.62). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately.