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Sustainability
Volume 17
Issue 10
10.3390/su17104711
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Review

Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting

by
Khushwant Singh
1,
Mohit Yadav
2,
Dheerdhwaj Barak
1,
Shivani Bansal
2 and
Fernando Moreira
3,4,*
1
Department of Computer Science & Engineering, University Institute of Engineering and Technology, M.D. University, Rohtak 124001, Haryana, India
2
Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali 140413, Punjab, India
3
Research on Economics, Management, and Information Technologies (REMIT), Universidade Portucalense, 4200-072 Porto, Portugal
4
Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Universidade de Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4711; https://doi.org/10.3390/su17104711
Submission received: 24 March 2025 / Revised: 29 April 2025 / Accepted: 13 May 2025 / Published: 20 May 2025
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Abstract

Fueled by scientific innovations and data-driven approaches, accurate agriculture has arisen as a transformative sector in contemporary agriculture. The present investigation provides a summary of modern improvements in machine-learning (ML) strategies utilized for crop prediction, accompanied by a performance exploration of contemporary models. It examines the amalgamation of sophisticated technologies, cooperative objectives, and data-driven methodologies designed to address the obstacles in conventional agriculture. The study examines the possibilities and intricacies of precision agriculture by analyzing various models of deep learning, machine learning, ensemble learning, and reinforcement learning. Highlighting the significance of worldwide collaboration and data-sharing activities elucidates the evolving landscape of the precision farming industry and indicates prospective advancements in the sector.
Keywords: crop prediction; machine learning; deep learning; smart farming; precision agriculture crop prediction; machine learning; deep learning; smart farming; precision agriculture

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MDPI and ACS Style

Singh, K.; Yadav, M.; Barak, D.; Bansal, S.; Moreira, F. Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability 2025, 17, 4711. https://doi.org/10.3390/su17104711

AMA Style

Singh K, Yadav M, Barak D, Bansal S, Moreira F. Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability. 2025; 17(10):4711. https://doi.org/10.3390/su17104711

Chicago/Turabian Style

Singh, Khushwant, Mohit Yadav, Dheerdhwaj Barak, Shivani Bansal, and Fernando Moreira. 2025. "Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting" Sustainability 17, no. 10: 4711. https://doi.org/10.3390/su17104711

APA Style

Singh, K., Yadav, M., Barak, D., Bansal, S., & Moreira, F. (2025). Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability, 17(10), 4711. https://doi.org/10.3390/su17104711

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