Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales, Iván; Fernández-Blanco, Enrique; Rivero, Daniel

doi:10.1080/00071668.2017.1379051

Cited by 16 publications

(15 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies reported using machine learning models to detect drops in flock-level egg production curves ( Morales et al., 2016 ; Ramírez-Morales et al., 2017 ). Compared with these studies, the current study could identify non-laying birds and facilitate monitoring of health and other issues at an individual bird level.…”

Section: Resultsmentioning

confidence: 99%

“…It consists of several processing units with characteristics like self-adapting, self-organizing, and real-time learning ( Ding et al., 2013 ). ANN has been widely used in poultry production, including estimation of broiler BW ( Amraei et al., 2017 ), total egg production of quails ( Felipe et al., 2015 ), and detecting drops in egg production of laying hens ( Ramírez-Morales et al., 2017 ). The objective of the current study was to improve the previous approach in 2 aspects: 1) To apply the model on the current day; 2) To output more informative results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens

et al. 2021

View full text Add to dashboard Cite

Identifying daily oviposition events for individual broiler breeders is important for bird management. Identifying non-laying birds in a flock that might be caused by improper nutrition or diseases can guide diet changes or disease treatments for these individuals. Oviposition depends on follicle maturation and egg formation, and follicle maturation can be variable. As such, the day and time of oviposition events of individual birds in a free-run flock can be hard to predict. Based on a precision feeding ( PF ) system that can record the feeding activity of individual birds, a recent study reported a machine learning model to predict daily egg-laying events of broiler breeders. However, there were 2 limitations in that study: 1) It could only be used to identify daily egg-laying events on a subsequent day; 2) The prediction outputs that were binary labels were unable to indicate more details among the outputs with the same label. To improve the previous approach, the current study aimed to predict and output the probability of daily oviposition events occurring using a specific time point in 1 day. In the current study, 706 egg-laying events recorded by nest boxes with radio frequency identification of hens and 706 randomly selected no-egg-laying events were used. The anchor point was newly defined in the current study as a specific time point in 1 day, and 26 features around the anchor point were created for all events (706 egg-laying events and 706 no-egg-laying events). A feed-forward artificial neural network ( ANN ) model was built for prediction. The area under the receiver operating characteristic ( ROC ) curve was 0.9409, indicating that the model had an outstanding classification performance. The ANN model could predict oviposition events on the current day, and the output was a probability that could be informative to indicate the likelihood of an oviposition event for an individual breeder. In situations where total egg production was known for a group, the ANN model could predict the probability of daily oviposition events occurring of all individual birds and then rank them to choose those most likely to have laid an egg.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Hypothesis tests should be performed to check the significance of differences between ANN configurations [40]. Table 3 shows that statistically significant differences were observed between different architectures.…”

Section: Resultsmentioning

confidence: 99%

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

et al. 2018

View full text Add to dashboard Cite

Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

show abstract

“…Data analysis in poultry systems has mainly been performed using mathematical and statistical methods or visual graph analyses. However, the high complexity of biological analyses leads to the use of newer techniques, which allow for the development of more robust systems against unexpected conditions [15]. In this context, the development of alternative and objective tools for the productive management of farms is essential to guarantee the productivity and health of the flocks.…”

Section: Discussionmentioning

confidence: 99%

“…Some previous studies cited in the literature performed ANNs as a prediction tool in this area. But, in general, the only parameters estimated were the total egg production or anomalies in egg production, selecting inferior number of parameters as input variables [2,8, 15,22]. Regarding poultry health, ANNs have already been used to predict the antimicrobial resistance of Escherichia coli strains [17], the evaluation of lymphocyte depletion in the Bursa of Fabricius [13] and as an objective criteria for histological diagnosis of lymphocytic losses in the thymus by the use of image analysis [5].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks on Eggs Production Data Management

Almeida

Oliveira

Furian

et al. 2020

Acta Scientiae. Vet.

View full text Add to dashboard Cite

Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian egg industry has been characterized by a constant production expansion in the last decade, increasing the number of housed animals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial Neural Networks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was at validating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. The data refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selected as “output” data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as “input” data (flock age, flock identification, total hens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen, weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programs NeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networks seeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through the comparison between the predictions and the real data present in the database that was the basis for the work. Validation of each ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error (MSE). For instance, R2 above 0.70 expresses a good validation. ANN developed for the output variable “number of dead birds per week” presented R2= 0.9533 and MSE= 256.88. For “feed consumption”, the results were R2= 0.7382 and MSE= 274.56. For “number of eggs (eggs/hen)”, the results were R2= 0.9901 and MSE= 172.26. For “weekly weight”, R2= 0.9712 and MSE= 11154.41. For “weekly egg production”, R2= 0.8015 and MSE= 72.60. For “flock uniformity”, R2= -2.9955 and MSE= 431.82.Discussion: From the six ANN designed in this study, in five it was possible to validate the predictions by comparing predictions with the real data. In one output parameter (“flock uniformity”), it was not possible to have adequate validation due to insufficient data in our database. For “number of dead birds per week”, “feed consumption”, “weekly weight” and “uniformity”, the most important variable was “flock age” (27.5%, 52.5%, 55.2% and 37.9%, respectively). For “number of eggs (eggs/hen)”, “uniformity” (52.1%) was the most relevant variable for prediction. For “weekly egg production”, “flock age” and “number of eggs (eggs/hen)” were the most important zootechnical parameters, both with a relative contribution of 38.2%. The results showed that even with the use of a robust tool such as ANNs, it is necessary to have well-noted and clear information that expresses the reality of the flocks. In any case, the results presented allow us to state that ANNs are capable for the management of data generated in a commercial egg production facility. The process of evaluation of these data would be improved if ANNs were routinely used by the professionals linked to this activity.

show abstract

Automated early detection of drops in commercial egg production using neural networks

Cited by 16 publications

References 44 publications

Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens

Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

Artificial Neural Networks on Eggs Production Data Management

Contact Info

Product

Resources

About