A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system

You, Jia-Huai; Lou, Edmond; Afrouziyeh, Mohammad; Zukiwsky, Nicole M.; Zuidhof, M. J.

doi:10.1016/j.compag.2021.106171

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…Those characteristics do not represent the normal variability observed in data from commercial broiler operations. A few studies have explored using RF to predict responses in poultry, mainly for broiler breeders under precision feeding [72][73][74] or using sound data [75]. Only a few reports have used broiler commercial data to predict relations among the variables [34,76], indicating that RF was also the best predictive methodology.…”

Section: Prediction Of Performance With MLmentioning

confidence: 99%

Effect of Environmental and Farm-Associated Factors on Live Performance Parameters of Broilers Raised under Commercial Tropical Conditions

Quintana-Ospina,

Alfaro-Wisaquillo,

Oviedo-Rondon

et al. 2023

Animals

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Although temperature, relative humidity, and farm-associated factors are known to affect broiler live performance, data about the impact of these variables under commercial operations are still scarce. This study aimed to evaluate the effect of temperature, relative humidity, a thermal humidity index, management, and farm-associated factors on BW, BW gain, feed conversion ratio (FCR), and mortality of broilers raised to 35 d under commercial tropical conditions. The data analyzed included performance records of Ross 308 AP broiler flocks placed between 2018 and 2020. Environmental monitoring information was obtained from electronic sensors that captured data hourly from 80 flocks in 29 farms. Farm-associated factors were gathered using a survey of 86 farms. Three data analyses were conducted in parallel. Correlation analyses, one-way ANOVA, and machine learning techniques were employed. Results indicated that BW and BW gain were reduced, and FCR worsened (p < 0.001) up to 21 d when chickens were mainly exposed to temperatures 2.5 °C lower than the recommended optimums for each age period. At the same time, mortality at 28 and 35 d increased. In conclusion, all farm-associated factors affected chicken live performance. Variable importance analysis indicated that performance results at 14 and 21 d were significant to predict BW at 35. At the same time, sex, distance between the hatchery and farm, and farm altitude accounted for the most significant contributions from the farm-associated factors.

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Section: Prediction Of Performance With MLmentioning

confidence: 99%

Effect of Environmental and Farm-Associated Factors on Live Performance Parameters of Broilers Raised under Commercial Tropical Conditions

Quintana-Ospina,

Alfaro-Wisaquillo,

Oviedo-Rondon

et al. 2023

Animals

View full text Add to dashboard Cite

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“…Analyzing data from wearable accelerometers using two machine learning models, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) [12], classified specific broiler behaviors. You et al [13] described a supervised machine learning method to detect anomalies in real-time broiler body weight recorded by the system. The tested machine learning algorithms were KNN, random forest classifier (RF), SVM, and artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Classifier’s Performance for Detecting the Pecking Pattern of Broilers during Feeding

et al. 2022

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Broiler feeding is an efficient way of evaluating growth performance, health, and welfare status. This assessment might include the number of meals, meal period, ingestion rate, meal intervals, and the proportion of time spent eating. These parameters can be predicted by studying the birds’ pecking activity. The present study aims to design, examine, and validate classifying algorithms to determine individual bird pecking patterns at the feeder. Broilers were reared from 1 to 42 days, with feed and water provided ad libitum. A feeder equipped with a force sensor was installed and used by the birds starting at 35 days of age, to acquire the pecking force data during feeding until 42 days. The obtained data were organized into two datasets. The first comprises 17 attributes, with the supervised attribute ‘pecking detection’ with two classes, and with ‘non-pecking’ and ‘pecking’ used to analyze the classifiers. In the second dataset, the attribute ‘maximum value’ was discretized in three classes to compose a new supervised attribute of the second dataset comprising the classes’ non-pecking, light pecking, medium, and strong. We developed and validated the classifying models to determine individual broiler pecking patterns at the feeder. The classifiers (KNN, SVM, and ANN) achieved high accuracy, greater than 97%, and similar results in all investigated scenarios, proving capable of performing the task of detecting pecking.

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“…Wen [14] presented the problem of detecting outliers in fixation gaze data through a novel mixed-integer optimization formulation and subsequently strengthened the formulation using two geometric arguments to provide enhanced bounds. Ji-hao [15] compared eight common anomaly detection methods based on the statistical distribution of data and features to detect anomalies in real-time body weight (BW) recorded by a precision feeding (PF) system. Ekin [16] proposed a new nonparametric outlier detection technique in the preprocessing stage of data analyses, which was based on the frequency-domain and Fourier transform definitions, called the frequency-domain based outlier detection (FOD).…”

Section: Introductionmentioning

confidence: 99%

Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

Ding

Liu

et al. 2021

Energies

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With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.

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A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system

Cited by 12 publications

References 21 publications

Effect of Environmental and Farm-Associated Factors on Live Performance Parameters of Broilers Raised under Commercial Tropical Conditions

Effect of Environmental and Farm-Associated Factors on Live Performance Parameters of Broilers Raised under Commercial Tropical Conditions

Classifier’s Performance for Detecting the Pecking Pattern of Broilers during Feeding

Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

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