Predictive Modeling of Poultry Growth Optimization in IoT-Connected Farms Using SVM for Sustainable Farming Practices

Modern agriculture uses the Internet of Things (IoT) to improve production and sustainability. This research predicts poultry growth on IoT-connected farms to enhance sustainable farming. Predictive models are created using Support Vector Machines (SVM), a machine learning approach, and poultry farm IoT sensor data. Temperature, humidity, feeding schedules, and disease prevalence are examined to improve poultry development. SVM models are trained and tested using historical poultry growth data and real-time IoT sensor data. The models forecast poultry growth and find abnormalities. The research uses SVM to help farmers make data-driven choices to boost poultry growth and avoid resource waste. It shows that SVM-based predictive modeling optimizes poultry growth in IoT-connected farms. These models improve poultry growth management, mortality rates, and resource use, promoting sustainable farming. Agriculture will be transformed by IoT and machine intelligence, enabling efficient and sustainable farming. This work advances precision agriculture in poultry, where data-driven decision-making and IoT connection are crucial to sustainable farming. The system can reach a 90% success rate in predicting poultry growth via real-time data analysis, which allows farmers to enhance their management techniques and environmental controls for sustainable farming.