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An Explainable AI-Driven Framework for Precision Agriculture: A Comprehensive Survey

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dc.contributor.authorDhotre, A.D.
dc.contributor.authorThorat, S.A.
dc.contributor.authorYelure, B.S.
dc.contributor.authorJawade, P.B.
dc.date.accessioned2026-04-29T07:53:57Z
dc.date.available2026-04-29T07:53:57Z
dc.date.issued2026
dc.descriptionReceived: December 30th, 2025 ; Accepted: March 31st, 2026 ; Published: April 21st, 2026 ; Correspondence: aakashdhotre12@gmail.com, sathorat2003@gmail.comeng
dc.description.abstractThis review focuses on crop recommendation systems and provides a thorough explanation of Explainable AI (XAI) in precision agriculture. The paper charts the development of predictive models that have been published in the literature, from straightforward, comprehensible algorithms to extremely accurate ‘black box’ ensemble and deep learning models, as well as their lack of transparency, which may erode farmers' confidence. In order to make these black box algorithms comprehensible and useful, the paper focuses on two XAI frameworks – LIME and SHAP – that are currently in use. The accuracy and explainability trade-off, problems with data heterogeneity, and the requirement for relevant user explanations are just a few of the significant gaps in the evidence base that are highlighted by the paper's synthesis of the research. The paper's concluding remarks provide a potential path toward integrated, reliable, and comprehensible AI systems that will enhance contemporary sustainable agriculture.eng
dc.identifier.citationDhotre, A. D., Thorat, S. A., Yelure, B. S., & Jawade, P. B. (2026). An Explainable AI-Driven Framework for Precision Agriculture: A Comprehensive Survey. Estonian University of Life Sciences. https://doi.org/10.15159/AR.26.022en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2026, vol. 24, Special Issue 1, pp. 147–163eng
dc.identifier.urihttp://hdl.handle.net/10492/10370
dc.identifier.urihttps://doi.org/10.15159/ar.26.022
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectcrop recommendation systemeng
dc.subjectdeep learningeng
dc.subjectexplainable AI (XAI)eng
dc.subjectLIMEeng
dc.subjectmachine learningeng
dc.subjectprecision agricultureeng
dc.subjectSHAPeng
dc.subjecttrustworthy AIeng
dc.subjectarticleseng
dc.titleAn Explainable AI-Driven Framework for Precision Agriculture: A Comprehensive Surveyeng
dc.typeArticleeng

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