Smart Platform Designed to Improve Poultry Productivity and Reduce Greenhouse Gas Emissions

Arhipova, Irina; Vītols, Gatis; Paura, Līga; Jankovska, Liga

doi:10.1007/978-981-16-2377-6_6

Cited by 5 publications

(1 citation statement)

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“…Our previous work includes the analysis and proposal of the smart poultry farm management system's design [13], the development of the cyber-physical model for data acquisition and processing [14], and the evaluation of egg production process in regard to production efficiency and greenhouse gas emissions [15]. The cyber-physical model in this context is a three-component system: a set of sensors to obtain measurements, data exchange controllers that fulfil the role of an intermediary between sensors and data centre, and a data centre as an analytical hub with decision making capabilities.…”

Section: Introductionmentioning

confidence: 99%

Hen Egg Production Forecasting: Capabilities of Machine Learning Models in Scenarios with Limited Data Sets

et al. 2023

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To achieve a sophisticated and self-sufficient production environment that aims to optimize a particular production sequence or direction, a combination of multiple interconnected IoT devices, software, and decision-making expertise is required. This is nowadays referred to as “smart” systems and can be related to almost any field. In the case of the poultry industry, “smart” stands for automatic data gathering, in-depth processing, and decision-making support. The implementation of a smart poultry concept introduces several challenges that are production related (e.g., productivity forecasting); therefore, this study focuses on hen egg production forecasting with limited data sets. Different methods and approaches used in the poultry sector for egg production forecasting were investigated. A cross-comparison was made between different models in order to determine their applicability. The models considered include a non-linear Modified Compartmental and several machine learning (ML) models, such as, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), XGBoost, and Random Forest (RF). Selected models used only two data sets—one for training and one for testing. Furthermore, the testing data set was significantly different than the training data, therefore setting the forecasting task to be even more challenging. The ML models had significantly more inputs that allowed them to adapt more flexibly to a changing environment in comparison with the nonlinear model that expected only one input, e.g., the week of egg production. The tests showed that the machine learning models proved to be overall more accurate than the selected nonlinear model.

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Section: Introductionmentioning

confidence: 99%