Prospect and scope of artificial neural network in livestock farming: a review

Rahman, Mohibur; Mandal, Ajoy; Gayari, Indrajit; Bidyalaxmi, Kangabam; Sarkar, D.; Allu, Teja; Debbarma, Asish

doi:10.1080/09291016.2022.2139389

Cited by 1 publication

(1 citation statement)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usage of artificial intelligence in the form of artificial neural networks (ANNs) in modelling dairy cow herd parameters is relatively new, but promising [19,31]. ANNs are employed to forecast various production [23,32,33], health [34,35], and behaviour indicators [36] that are essential for managing dairy cow herds [37,38].…”

Section: The Use Of Neural Network In Modelling Several Cattle Herd P...mentioning

confidence: 99%

Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland

et al. 2023

View full text Add to dashboard Cite

In the presented study, data regarding the size and structure of cattle herds in voivodeships in Poland in 2019 were analysed and modelled using artificial neural networks (ANNs). The neural modelling approach was employed to identify the relationship between herd structure, biogas production from manure and slurry, and the geographical location of herds by voivodeship. The voivodeships were categorised into four groups based on their location within Poland: central, southern, eastern, and western. In each of the analysed groups, a three-layer MLP (multilayer perceptron) with a single hidden layer was found to be the optimal network structure. A sensitivity analysis of the generated models for herd structure and location within the eastern group of voivodeships revealed significant contributions from dairy cows, heifers (both 6–12 and 12–18 months old), calves, and bulls aged 12–24 months. For the western voivodeships, the analysis indicated that only dairy cows and herd location made significant contributions. The optimal models exhibited similar values of RMS errors for the training, testing, and validation datasets. The model characterising biogas production from manure in southern voivodeships demonstrated the smallest RMS error, while the model for biogas from manure in the eastern region, as well as the model for slurry in central parts of Poland, yielded the highest RMS errors. The generated ANN models exhibited a high level of accuracy, with a fitting quality of approximately 99% for correctly predicting values. Comparable results were obtained for both manure and slurry in terms of biogas production across all location groups.

show abstract