Abstract—Animal agricultural productivity is highly influenced by the environment with many variables that affect it which makes forecasting a challenging task in this industry. We propose a new and practical approach forecasting egg-supply by utilizing Machine Learning (ML) algorithms, powered by limited data, which is regularly collected in this industry. The proposed approach does not require additional organizational resources for the purpose of collecting information but examines the behavior of farmers as expressed in the supply of eggs to the Egg Sorting Institute (ESI). We propose several possible models and present forecasts for egg supply with 90% accuracy, thus allowing both effective and economical operational decisions to be made.

Keywords—egg, poultry, forecasting, machine learning

Cite: Avraham Cohen and Shay Horovitz, "Machine Learning Based Egg Supply Forecasting for Sorting and Grading Institutes," Journal of Advanced Agricultural Technologies, Vol. 10, No. 2, pp. 52-58, December 2023. doi: 10.18178/joaat.10.2.52-58

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