Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning

Abstract

The fuel consumption model serves as a valuable tool for estimating real-time fuel consumption levels. An accurate fuel consumption model is crucial in providing precise information regarding fuel utilisation and motivating experts to assess the vehicle’s economy. The goal of this study was to develop a novel approach by eliminating the measurement of real-time tractor PTO (power take-off) power for fuel consumption prediction and to develop a cloud-infused, server-less, machine learning (ML) based real-time generalised tractor fuel consumption prediction model for any tractor between the power range of 8–48 hp. The fuel consumption prediction models from 18 Machine Learning algorithms were developed, and the extensive data analysis with hyperparameter tuning concluded that the Gradient Boosting Regressor Machine Learning model outperformed the other Machine Learning models with reasonable accuracy (R² = 0.999 for training and 0.914 for testing). Cloud-based serverless Web App and Android App integrated with the Gradient Boosting Regressor based fuel consumption prediction model were developed for the real-time fuel consumption prediction and monitoring of a tractor during field operations. The developed Machine Learning model predicted fuel consumption with a Mean Absolute Percentage Error of 11.43% during real-time field experiments with Mouldboard plough operation. The field validation showed the generalisation ability and efficacy of the developed model, and it can be implemented as a user advisory system for real-time energy-efficient agricultural operations.

Graphical Abstract

Keywords:

• Tractor fuel consumption prediction
• generalised model
• machine learning
• gradient boosting regressor
• cloud-driven serverless app
• android app

Reviewing Editor:

• Pham D T, University of Birmingham, UK

SUBJECTS:

• Agriculture & Environmental Sciences, Artificial Intelligence, Computation, Information & Communication Technology (ICT), Power & Energy, Technology

Acknowledgements

The authors would acknowledge the sincere appreciation for the research facilities of ICAR Project – CRP on Engineering Interventions in Precision Farming and Micro Irrigation Systems (PF & MIS).

Authors’ contributions

Harsh Nagar: conceptualization, methodology, data curation, formal analysis, software, writing–original draft, writing–review & editing, validation. Rajendra Machavaram: conceptualization, methodology, writing–original draft, supervision. Ambuj: writing–review & editing, software. Peeyush Soni: formal analysis, supervision. Vijay Mahore: methodology, validation. Prakhar Patidar: formal analysis. All authors affirm their shared responsibility and commitment to be accountable for all facets of the research work.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Harsh Nagar

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.

Rajendra Machavaram

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.

  Ambuj

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.

Peeyush Soni

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.

Vijay Mahore

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.

Prakhar Patidar

Harsh Nagar, Ambuj, Vijay Mahore and Prakhar Patidar are research scholars at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. Rajendra Machavaram and Peeyush Soni are Associate Professors at the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India.