AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage

Tractor engine performance is critical for optimizing energy efficiency in tillage operations across various soil conditions. This study introduces an artificial neural network (ANN) based novel approach for predicting and optimizing specific fuel consumption (SFC) and fuel consumption per tilled area (FCA), during tillage operations. Field tests were conducted to collect the data on rear axle torque, forward velocity, fuel consumption, throttle position, engine speed, and depth of operation under soft, medium, and hard soil conditions while using a reversible MB plough, offset disc harrow, and rigid tyne cultivator. Tractor engine performance was predicted using ANN and optimized by matching the required rear axle power with the available rear axle power. Optimal operating parameters for three distinct tillage implements, each associated with varying soil types and tillage depths, were determined based on minimizing SFC and FCA. An integrated ANN-based user advisory system was developed to provide tractor engine performance predictions and recommendations for optimal operating parameters to maximize fuel efficiency. The recommendations were validated with actual field data, resulting in Mean Absolute Percentage Errors of 7.56% and 8.98% for MB plough, 11.19% and 9.36% for offset disc Harrow, and 10.90% and 10.57% for cultivator, respectively, for SFC and FCA. ANN-based novel approach is proposed to optimize fuel efficiency during tillage Advisory system is developed to recommend parameters for optimum tillage Suggestions for minimum SFC and FCA are provided in soft, medium and hard soil The accuracy and reliability of advisory system are proven by field validation