A Heuristic and Data Mining Model for Predicting Broiler House Environment Suitability

SIMPLE SUMMARY: The broiler housing control environment now is primarily based on the rearing temperature. The current study proposes two decision-tree models using flock-based and environmental data such as ambient temperature, air velocity, relative humidity, and ammonia concentration. Data from commercial broiler farms were collected and analyzed. An exploratory analysis employed the environmental variables, and a heuristic approach was used to develop a final dataset based on ammonia concentration’s impact on broiler production. The output models were related to dry bulb temperature, relative humidity, air velocity, and ammonia concentration arrays. The resulting trees classify the most suitable commercial broiler environment. Such variable combinations might help to improve environmental control in broiler houses. ABSTRACT: The proper combination of environment and flock-based variables plays a critical role in broiler production. However, the housing environment control is mainly focused on temperature monitoring during the broiler growth process. The present study developed a novel predictive model to predict the broiler (Gallus gallus domesticus) rearing conditions’ suitability using a data-mining process centered on flock-based and environmental variables. Data were recorded inside four commercial controlled environment broiler houses. The data analysis was conducted in three steps. First, we performed an exploratory and descriptive analysis of the environmental data. In the second step, we labeled the target variable that led to a specific broiler-rearing scenario depending on the age of the birds, the environmental dry-bulb temperature and relative humidity, the ammonia concentration, and the ventilation rate. The output (final rearing condition) was discretized into four categories (‘Excellent’, ‘Good’, ‘Moderate’, and ‘Inappropriate’). In the third step, we used the dataset to develop tree models using the data-mining process. The random-tree model only presented accuracy for predicting the ‘Excellent’ and ‘Moderate’ rearing conditions. The decision-tree model had high accuracy and indicated that broiler age, relative humidity, and ammonia concentration play a critical role in proper rearing conditions. Using a large amount of data allows the data-mining approach to building up ‘if–then’ rules that indicate suitable environmental control decision-making by broiler farmers.