Poultry farming in developing countries is a significant source of revenue generation for smallholder farmers. Also, raising poultry in Africa plays a vital role in providing the protein requirements for humans through its meat and eggs [1] .

In modern times, farmers make use of egg incubators to hatch their eggs. This device provides ideal temperature and humidity in an enclosed environment for egg embryo development without human intervention [2] .

In poultry farming, maximizing the effectiveness of incubation, early identification of viable eggs, reducing cost on wasted/spoiled eggs and boosting overall egg hatching while minimizing resource waste are very crucial to every farmer [3] . In recent times, Machine learning is used in the poultry industry to predict egg viability and hatchability, but much has not been achieved in Ghana locally. This technology has been shown to achieve accuracy rates of over 90% in predicting hatchability, making it a useful tool for hatcheries to optimize production and reduce waste and costs [4] . The use of artificial intelligence, machine learning, among other innovative tools, has allowed us to predict early hatching rates with remarkable accuracy by improving hatchability. Electricity, time and space are saved by preventing unnecessary waiting for infertile eggs in the incubator not to hatch [5] .

This study aims to develop a hatch prediction model using linear regression to help farmers select good eggs for incubation to avoid wastage of eggs, time and other resources.

Incubation is a critical aspect of poultry farming, as it allows farmers to hatch eggs under controlled conditions and ensure the best possible hatching rates. It is therefore necessary that farmers are able to predict the hatchability of the eggs to be incubated. Machine learning algorithms such as regression, decision trees or neural networks can be used to create a model that can predict the optimal results based on the data that one collected. [6] considered the online-prediction kind of standard problem of linear regression. The objective of the researchers was to estimate subsequent responses given the relevant explanatory variables and all of the prior observations. The researchers were mainly interested in predicting intervals instead of point predictions. [7] opined that the prediction distribution is a good basis for predictive inferences applied to several real-world situations. This is a distribution of the unobserved future response(s) conditional on a set of realized responses from an informative experiment. The Bayesian approach was used by the researchers to further derive the prediction distribution(s) for the multiple linear regression model. The Bayesian approach was used by the researchers to further derive the prediction distribution(s) for the multiple linear regression model. Linear regression is a popular machine learning-based prediction tool used in agriculture to predict crop yield. A study was conducted by [8] aimed to predict corn yield using linear regression. The study utilized various predictors, such as soil characteristics, weather conditions, and crop management practices, among others. The results showed that linear regression is a very powerful tool in the prediction of crop yield. The study found that soil characteristics were also significant predictors of corn yield.

Machine learning techniques have also been applied to poultry productivity prediction. [9] conducted a review of various machine learning methods, including linear regression, neural networks and support vector machines for this purpose. Their study found that linear regression models are effective in predicting poultry productivity when appropriate features are selected and model parameters are optimized. Another literature review on this topic was conducted by [10] , who discussed the various types of linear regression models that have been used for predicting poultry performance. They analyzed the strengths and limitations of different models, such as simple linear regression, multiple linear regression, and polynomial regression, and concluded that the choice of model depends on the specific prediction task and data characteristics. Furthermore, [11] examined the application of linear regression models in predicting egg hatchability. Their study revealed that linear regression models can effectively predict egg hatchability by incorporating various factors, such as incubation temperature, humidity, and egg weight. They also highlighted the importance of using appropriate feature selection techniques and sample sizes to ensure accurate predictions. A study by [12] investigated the effectiveness of linear regression models in predicting egg hatchability in poultry farming. The authors analyzed various factors that impact egg hatchability, including egg weight, egg shape index, and incubation temperature. They developed a multiple linear regression model to predict hatchability and compared its performance with that of a simple linear regression model. The study found that the multiple linear regression model achieved a higher accuracy in predicting egg hatchability than the simple linear regression model. However, the authors also noted that the predictive power of the model was affected by the quality and quantity of the input data, as well as the model parameters.

After a critical review of related studies, it has been established that Linear Regression is a crucial tool in machine learning as it has been used in numerous prediction studies. Overall, machine learning has the potential to revolutionize the poultry farming industry by providing insights into the production process, predicting outcomes with high accuracy, improving efficiency, disease management, and animal welfare. While there are challenges associated with implementing machine learning in poultry farming, such as data availability and model interpretability, the benefits are significant and warrant further investigation.

This study aimed to predict egg hatch rate using linear regression. The study collected data on egg viability/hatchability history from two hatchery centers in Tamale in Northern Region and Kumasi in the Ashanti Region of Ghana. The study combines data from Tamale (extreme weather) and Kumasi (favorable weather), making it robust and representative for conclusions across Ghana.

One thousand and fifty (1050) dataset was collected from the two centers. From the data collected, several independent variables were extracted, including quantity of eggs processed, breed of bird, age of birds, breeding nature (free range/intensive), egg storage temperature, egg duration of storage, transport distance, average size of egg collected, fertility and hatch rate, with hatch rate as the dependent variable. These features were considered crucial by the researcher as they are major factors that drive egg hatch rate in the incubation. Table 1 below shows the various descriptions of the variables that were used in this study.

The Hatch Rate Prediction Model is a critical component of our Indigenous Smart Incubator, which uses machine learning algorithms to analyze and predict the viability of eggs in real-time. The goal of our model is to improve hatch rates, reduce production costs, and increase productivity in the poultry industry.

To establish a reliable model, the data collected were wrangled and subsequently trained using Linear Regression to ensure accurate model generation. The independent variables, which include quantity of eggs, breed of bird, age of egg, mode of breeding, storage temperature, duration of storage, egg transport distance, average size of egg and egg fertility, were subjected to heatmap correlation analysis, which yielded the following result.

Figure 1 indicates that maximum correlation of 0.41. This indicates there is no strong correlation between the independent variables and also, this suggests that linear regression can be applied to the dataset as there is no multicollinearity between the independent variables.

Table 2 indicates that there is no strong positive or negative correlation between the independent variables. The maximum correlation 0.2615 exists between the free range and transport distance, which is still minimal and hence we can affirm that there is no multicollinearity between the independent variables. No multicollinearity between the independent variables implies that Linear Regression can be applied to the dataset.

This study aimed to develop a prediction model for estimating the egg hatch rate for local farmers using a linear regression model in machine learning. Through the collection and analysis of relevant data, including various independent variables such as the quantity of eggs processed, breed of bird, age of birds, breeding nature, egg storage temperature, egg duration of storage, transport distance, average size of eggs collected, fertility, and hatch rate, we were able to derive valuable insights.

The correlation analysis, as demonstrated by the heatmap and pair plot, revealed a lack of multicollinearity among the independent variables, indicating that they contributed unique information to the prediction model. The R² value of 0.6781 indicated that approximately 67.81% of the variability in the egg hatch rate could be explained by the selected independent variables. Furthermore, the mean squared error of 14.37 indicated that the model’s predictions were reasonably accurate. This study ultimately makes a significant contribution to its scope as it helps farmers in Ghana appropriately predict egg hatch rates, thereby minimizing egg losses in the process of incubation.