Federated learning for crop yield prediction: A comprehensive review of techniques and applications.

Vani Hiremani, Raghavendra M Devadas, Preethi, R Sapna, T Sowmya, Praveen Gujjar, N Shobha Rani, K R Bhavya

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In one paragraph

Review in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Vani HiremaniSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India.

Raghavendra M DevadasDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

PreethiDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

R SapnaDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

T SowmyaDepartment of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

Praveen GujjarFaculty of Management Studies, JAIN (Deemed-to-be University), Bengaluru, India.

N Shobha RaniMURTI Research Center, Smart Agriculture Labs, Department of Artificial Intelligence and Data Science, GITAM School of Technology, GITAM (Deemed to be) University, Bengaluru, India.

K R BhavyaDepartment of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be) University, Bengaluru, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy-making it highly applicable technology for sensitive agricultural data. This paper offers a systematic overview of the recent knowledge on the application of FL towards the prediction of crop yield and other agricultural uses. We discussed the mathematical basis of FL, the variety of machine learning models used, the types of used agricultural data, and the major performance metrics. The paper presents real-world applications and lists the current limitations, including communication overhead, data heterogeneity, and interpretability issues. Lastly, we introduce open research directions to inform the development of FL in precision agriculture.

Indexed as

Crop yield predictionData privacyFederated learningMachine learningPrecision agricultureRice cultivation

Identifiers

PMID40529517

DOI10.1016/j.mex.2025.103408

PMCPMC12171546

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.