Author(s)

Abraham Atosona, Stephen Akobre, Mohammed Ibrahim Daabo

Department of Computer Science, Department of Cyber Security and Computer Engineering Technology, School of Computing and Information Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.

Egg loss is one of the major problems in the egg hatching industry. This study aims to support farmers in optimizing their egg hatch through the development of a prediction model. This is to enable local farmers to accurately predict the hatchability of eggs before loading them into the incubator, thereby minimizing losses and reducing time wastage. By employing advanced data analysis techniques, a predictive algorithm was developed to predict the hatchability of eggs based on various parameters, such as egg storage temperature, time of egg storage, quantity processed and other egg characteristics. This predictive model provides farmers with valuable insights into the potential outcome of the incubation process, enabling them to make informed decisions in selecting eggs for incubation. The prediction model exhibited a high level of accuracy in estimating hatchability for farmers. The practical implications of this research are significant as it helps local farmers minimize losses, reduce time wastage and improve the overall efficiency of egg hatch process. This research does not only contribute to the field of agricultural technology, but also provides practical solutions for sustainable farming practices.

Hatchability, Algorithm, Incubation, Prediction

Atosona, A., Akobre, S. and Daabo, M. I. (2025) A Prediction Model for Estimating Egg Hatch Rate for Ghanaian Farmers Using Machine Learning. Journal of Data Analysis and Information Processing, 13, 306-313. doi: 10.4236/jdaip.2025.133019.