The agricultural industry has adopted modern machinery for key operations which has made equipment reliability important for productivity and minimizing downtime. The use of traditional maintenance approaches which are reactive or time-based precision are insufficient for modern agriculture. This paper looks into the application of IoT technology with machine learning algorithms for predictive maintenance systems customized for agricultural equipment. By placing IoT sensors on tractors, harvesters and other equipment, critical operational data like temperature, vibration, fuel consumption, and other usage patterns can be monitored 24/7. Machine learning models analyze this data for anomaly detection, predicting component failures, and formulating maintenance schedules. This enhances maintenance scheduling by proactively addressing issues to minimize equipment failures, improving the life span of the machinery, and improving the decision making processes at the farm level. This research proposes an IoT-ML framework for predictive maintenance and validates it through simulations coupled with a case study of mid-sized farming businesses. The research shows improved operational efficiency, cost savings, and increased equipment access for farmers. The findings of this research also highlight data connectivity limitations for rural areas as well as the need to equip farmers with digitals skills. The main conclusion of the work presented in this paper is the application of machine learning for IoT devices empowers farmers to make data driven decisions, integrating sustainability and enhancing operational practices in agriculture.
 

Predictive Maintenance, Agriculture, IoT (Internet of Things), Machine Learning, Farm Equipment, Operational Efficiency, Smart Farming