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Review

Intelligent Agriculture for Plant Disease Management: A Critical Review of Operational Value, Field Reliability, Adoption Barriers, and Research Priorities

Bouazizi Emna ORCID ,  Gharbi Yaakoub ORCID
    Received: 24 June 2026; Revised: 12 August 2026; Accepted: 26 August 2026; Published: 28 October 2026

    Abstract

    Plant disease management increasingly integrates artificial intelligence (AI), Internet of Things (IoT) sensing, remote and proximal sensing, disease forecasting, robotics, cloud–edge computing, and decision-support systems (DSS). This critical narrative review evaluated their operational value, reliability under production conditions, barriers to adoption, and priorities for improving field performance. An initial 99-record bibliography was screened, yielding 45 eligible sources; targeted searches of DOI/indexing services and publisher databases from 2010 to July 2026, while retaining earlier foundational studies, identified 35 additional DOI-verified sources, producing an 80-reference corpus. Evidence was appraised across five deployment-oriented domains: field representativeness, diagnostic ground truth, comparator quality, uncertainty and failure reporting, and operational outcomes. Intelligent tools provide three main benefits: earlier or more scalable detection, continuous and spatially explicit risk assessment, and targeted intervention. Field evidence is strongest for established disease-warning systems and selected precision-spraying applications, whereas image classifiers, foundation models, and autonomous robots remain more commonly validated in curated or crop-specific settings. Distribution shift is a major reliability concern, with commercial-field studies showing substantial performance losses under open-set conditions. Wider adoption is constrained by interoperability, cost, maintenance, connectivity, data governance, trust, liability, and unequal access. The most credible near-term pathway is supervised, context-sensitive autonomy. Future progress requires multi-farm and multi-season validation, explicit uncertainty reporting, FAIR (Findable, Accessible, Interoperable, and Reusable) data architectures, economic evaluation, climate-adaptive hybrid models, and end-to-end comparisons with best conventional practice. Intelligent agriculture should therefore be developed as accountable plant-health infrastructure that keeps plant pathologists, advisers, and farmers in the decision loop.

    Keywords

    References

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