Abstract
This research is done to demonstrate the applicability of machine learning models in automatic prediction of egg production in poultry farm systems. Unprocessed data were collected from a commercial egg farm daily over a period of one year. Other similar works require prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. The present approach will help the administrator of the farm to track the egg production in the poultry farm system for the whole farm or a specific shed for better utility in ensuring sustainable supply chain management for the industry. The primary objective of automating this prediction system is to provide a more accurate, efficient, and reliable outcome. While developing this machine learning model for forecasting, we utilized the ARIMA model under time series analysis and it is implemented and coded in Python using various relevant libraries. The accuracy of the proposed model is 59.21% for monthly analysis.
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Acknowledgements
The authors would like to acknowledge Professor Supriya Aras and Professor Surabhi Thatte for guiding us in the implementation, Mr. Dnyanraj Dhamne and Ms. Namrata Dalai for helping us in the extraction and preprocessing of data.
We would also like to express our gratitude and acknowledge that the data used in this research project was provided by ‘Venky's India Pvt. Ltd’ purely for this study only (www.venkys.com).
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Tikiwala, V., Khule, S., Nadgauda, C., Thatte, S. (2023). Automatic Prediction of Egg Production in Poultry Farm System. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_12
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