Vehicle data analytics system for agricultural equipment optimization and predictive maintenance

Overview

Modern agricultural equipment generates substantial sensor data that, when properly analyzed, can significantly impact farm profitability. Equipment failure downtime during harvest can cost hundreds of thousands of dollars in lost productivity and rental expenses. This research proposes a vehicle data analytics system that leverages onboard sensor data, edge computing, and artificial intelligence to deliver predictive maintenance, improved vehicle utilization, and optimized harvest planning for agricultural operations.

The solution addresses a critical need in modern farming: transforming raw equipment diagnostic data into actionable insights. By applying statistical analysis, vehicle simulation, and machine learning algorithms to data collected from active tractors, the system aims to discover patterns in vehicle performance, efficiency, and reliability that can reduce equipment failure risk and increase overall yield.

Technical specifications

Data collection infrastructure:

• PolyCAN, an open-source software tool developed as part of this research, extracts data directly from a tractor's diagnostic port
• Additional positioning sensors, cameras, and onboard diagnostic computers installed on selected test tractors
• Radio-linked data aggregator that collects and consolidates information from multiple vehicles in the field

Analytics capabilities:

• Statistical analysis of vehicle performance metrics across stratified samples of over 100 active tractors
• Vehicle simulation modeling to predict behavior under various operating conditions
• Artificial intelligence algorithms for optimization, planning, and scheduling
• Machine learning models trained to identify patterns in efficiency and reliability

Targeted outcomes:

• Predictive maintenance recommendations to reduce equipment failure risk
• Vehicle efficiency improvements through optimized utilization
• Yield optimization through better planning and scheduling
• Foundation for future autonomous tractor retrofitting based on collected data patterns

Technology readiness level

This research is currently at an early-to-mid stage of development. The PolyCAN open-source tool has been created and preliminary proof-of-concept data scans have been successfully collected from a tractor, demonstrating feasibility of the data extraction approach. Future validation involves outfitting test tractors from Cal Poly's agricultural operations with additional sensors and data collection hardware, then applying analytics to discover meaningful patterns. If exploratory results show promise, the project will scale to a larger autonomous vehicle retrofit initiative. The system represents a practical, applied research effort with clear pathways to commercial deployment in agricultural operations.