Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning

Abstract

The traditional Control Shed Poultry Farm (CSPF) systems are operated manually or in a semi-automatic culture. The existing CSPF system is still not free from human intervention and needs 24/7 monitoring. The existing approach is laborious, prone to caretakers bias and human error, and repeatability is difficult. The Intelligent CSPF system is based on the Internet of Things (IoT) and Machine Learning (ML). The proposed approach comprises the learning and mastering phase. In the learning phase, the system will observe or learn from user behaviour patterns regarding poultry activity operations for a specific time duration. Upon completion of the learning phase, the mastering phase will eventually automate the system and control the poultry farm's environmental parameters, such as temperature, humidity, water level, light, and hazardous gases based on the historical data. The proposed system will replicate the operator's past business control behavior. For this learning task, we have used Supervised ML techniques to analyze the performance of ML algorithms such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (k-NN), Support Vector Machine (SVM), and Naïve Bayes (NB). DT is the successful ML classifier with the highest accuracy performed in the Intelligent CSPF System; this leads to the development of a smart CSPF culture where successful business experiences, i.e. models, will be exchanged among the community to get a collective benefit in terms of revenue.