Ai for rice leaf disease detection: benchmarks, bias, optimization, and deployment readiness

Abstract Rice leaf diseases threaten yield, grain quality, and field-management decisions across intensive and smallholder production systems. Visual scouting remains essential, but it is labor-intensive, subjective, and difficult to scale under variable light, cultivar, growth-stage, and disease-pressure conditions. This article analyzes a curated corpus of 111 image-based rice leaf disease studies identified through a PRISMA-ScR-aligned search conducted during 1 January 2026–31 March 2026, with references restricted to publications available on or before 31 March 2026. The synthesis covers disease symptoms, search methodology, dataset provenance, preprocessing, classical machine learning, deep learning, transformer and hybrid architectures, bio-inspired optimization, explainability, severity estimation, and deployment readiness. Its central contribution is to separate clean benchmark performance from field-relevant evidence by examining data source, split unit, leakage risk, class imbalance, external validation, computational cost, and agronomic usefulness. The analysis shows that high internal accuracy is common on curated three- or four-class datasets, whereas cross-dataset validation, leakage-resistant splitting, class-wise reporting, severity localization, device-level efficiency metrics, and expert-validated explanations remain inconsistent. The article proposes a normalized benchmarking framework, an unweighted study-quality and interpretive-risk rubric, a leakage-aware dataset interpretation scheme, and a standardized reporting checklist for optimization-aware rice disease models. Progress in this area should be judged by reproducible, geographically diverse, severity-aware, explainable, and edge-feasible systems that support practical crop-health decisions, not by marginal gains on clean internal splits alone.