2019
1.
Bhole, Amey; Falzon, Owen; Biehl, Michael; Azzopardi, George
A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, image classification, pattern recognition, smart farming
@inproceedings{bhole2019computer,
title = {A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle},
author = {Amey Bhole and Owen Falzon and Michael Biehl and George Azzopardi},
doi = {https://doi.org/10.1007/978-3-030-29891-3_10},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
volume = {11679},
pages = {108--119},
organization = {Springer},
abstract = {The monitoring of farm animals is important as it allows farmers keeping track of the performance indicators and any signs of health issues, which is useful to improve the production of milk, meat, eggs and others. In Europe, bovine identification is mostly dependent upon the electronic ID/RFID ear tags, as opposed to branding and tattooing. The RFID based ear-tagging approach has been called into question because of implementation and management costs, physical damage and animal welfare concerns. In this paper, we conduct a case study for individual identification of Holstein cattle, characterized by black, brown and white patterns, in collaboration with the Dairy campus in Leeuwarden. We use a FLIR E6 thermal camera to collect an infrared and RGB image of the side view of each cow just after leaving the milking station. We apply a fully automatic pipeline, which consists of image processing, computer vision and machine learning techniques on a data set containing 1237 images and 136 classes (i.e. individual animals). In particular, we use the thermal images to segment the cattle from the background and remove horizontal and vertical pipes that occlude the cattle in the station, followed by filling the blank areas with an inpainting algorithm. We use the segmented image and apply transfer learning to a pre-trained AlexNet convolutional neural network. We apply five-fold cross-validation and achieve an average accuracy rate of 0.9754 ± 0.0097. The results obtained suggest that the proposed non-invasive approach is highly effective in the automatic recognition of Holstein cattle from the side view. In principle, this approach is applicable to any farm animals that are characterized by distinctive coat patterns.},
keywords = {convnets, deep learning, image classification, pattern recognition, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
The monitoring of farm animals is important as it allows farmers keeping track of the performance indicators and any signs of health issues, which is useful to improve the production of milk, meat, eggs and others. In Europe, bovine identification is mostly dependent upon the electronic ID/RFID ear tags, as opposed to branding and tattooing. The RFID based ear-tagging approach has been called into question because of implementation and management costs, physical damage and animal welfare concerns. In this paper, we conduct a case study for individual identification of Holstein cattle, characterized by black, brown and white patterns, in collaboration with the Dairy campus in Leeuwarden. We use a FLIR E6 thermal camera to collect an infrared and RGB image of the side view of each cow just after leaving the milking station. We apply a fully automatic pipeline, which consists of image processing, computer vision and machine learning techniques on a data set containing 1237 images and 136 classes (i.e. individual animals). In particular, we use the thermal images to segment the cattle from the background and remove horizontal and vertical pipes that occlude the cattle in the station, followed by filling the blank areas with an inpainting algorithm. We use the segmented image and apply transfer learning to a pre-trained AlexNet convolutional neural network. We apply five-fold cross-validation and achieve an average accuracy rate of 0.9754 ± 0.0097. The results obtained suggest that the proposed non-invasive approach is highly effective in the automatic recognition of Holstein cattle from the side view. In principle, this approach is applicable to any farm animals that are characterized by distinctive coat patterns.
2018
2.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel F; Petkov, Nicolai
Assigning pigs to uniform target weight groups using machine learning Inproceedings
Abstract | Links | BibTeX | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018assigning,
title = {Assigning pigs to uniform target weight groups using machine learning},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel F Veerkamp and Nicolai Petkov},
url = {https://research.rug.nl/en/publications/assigning-pigs-to-uniform-target-weight-groups-using-machine-lear},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the World Congress on Genetics Applied to Livestock Production, vol. Species-Porcine},
volume = {1},
pages = {112},
abstract = {A standard practice at pig farms is to assign finisher pigs to groups based on their live weight measurements or based on visual inspection of their sizes. As an alternative, we used machine learning classification, namely the random forest algorithm, for assigning finisher pigs to groups for the purpose of increasing body weight uniformity in each group. Instead of relying solely on weight measurements, random forest enabled us to combine weight measurements with other phenotypes and genetic data (in the form of EBV’s). We found that using random forest with the combination of phenotypic and genetic data achieves the lowest classification error (0.3409) in 10-fold cross-validation, followed by random forest with phenotypic and genetic data separately (0.3460 and 0.4591), then standard assignment based on birth weight (0.5611), and finally standard assignment based on the weight at the start of the finishing phase (0.7015).},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
A standard practice at pig farms is to assign finisher pigs to groups based on their live weight measurements or based on visual inspection of their sizes. As an alternative, we used machine learning classification, namely the random forest algorithm, for assigning finisher pigs to groups for the purpose of increasing body weight uniformity in each group. Instead of relying solely on weight measurements, random forest enabled us to combine weight measurements with other phenotypes and genetic data (in the form of EBV’s). We found that using random forest with the combination of phenotypic and genetic data achieves the lowest classification error (0.3409) in 10-fold cross-validation, followed by random forest with phenotypic and genetic data separately (0.3460 and 0.4591), then standard assignment based on birth weight (0.5611), and finally standard assignment based on the weight at the start of the finishing phase (0.7015).
3.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel; Petkov, Nicolai
Predicting slaughter age in pigs using random forest regression Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018predictingb,
title = {Predicting slaughter age in pigs using random forest regression},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel Veerkamp and Nicolai Petkov},
doi = {10.3233/978-1-61499-929-4-1},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Applications of Intelligent Systems 2018},
pages = {1-9},
organization = {IOS Press},
abstract = {Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.
4.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel F; Petkov, Nicolai
Predicting Slaughter Weight in Pigs with Regression Tree Ensembles. Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018predicting,
title = {Predicting Slaughter Weight in Pigs with Regression Tree Ensembles.},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel F Veerkamp and Nicolai Petkov},
doi = {10.3233/978-1-61499-929-4-1},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Frontiers in Artificial Intelligence and Applications},
volume = {310},
pages = {1--9},
abstract = {Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.