Remote sensing applications for Lettuce (Lactuca sativa L) across field and controlled environments: a review

Remote sensing is transforming lettuce ( Lactuca sativa L.) production by enabling quick, non-destructive measurements for better management in both open fields and controlled environments. This review synthesizes lettuce-focused studies published from 2005 to 2025 across six domains: (i) breeding and phenotyping, (ii) crop management operations (weed mapping, plant spacing, harvest delineation), (iii) crop water use estimation and stress diagnosis, (iv) pest and disease detection, (v) screening for chemical residues and heavy metals, and (vi) nutritional status assessment. Evidence is derived from satellite, Unmanned Aerial Vehicle (UAV), and proximal platforms using RGB, multispectral/red-edge, hyperspectral, thermal, and chlorophyll fluorescence imaging. Across applications, workflows typically move from pre-processing and calibration to feature extraction, then to modeling, and finally to actionable outputs such as trait maps, variable-rate scouting, irrigation guidance, and early warning of stress. However, adoption is constrained by limited model transferability across seasons, sites, and cultivars; confounding signals from overlapping stresses; inconsistent calibration and validation protocols; and practical barriers, including operator skill requirements and high capital and integration costs. To bridge research and practice, this study provides a consolidated table linking sensing modalities to specific lettuce decisions, summarizes compact red-edge/NIR field workflows, highlights the need for cultivar-aware modeling, and outlines two-tier concepts for residue screening. Future progress will depend on standardized protocols, open datasets, multi-season benchmarking, interoperable sensing systems, and targeted training to speed adoption. Finally, this review advances prior literature by providing a lettuce-specific, decision-oriented comparison across field and controlled environments and by linking sensing modalities to management actions, trade-offs, and adoption constraints.