Sparse-View Neural Radiance Reconstruction for Artificial Intelligence Agriculture Canopy Phenotyping Under Wind-Induced Motion: A Computer Geometric-Fidelity Evaluation

Neural radiance fields (NeRF) promise dense three-dimensional canopy reconstruction from sparse camera views, but field-deployed imaging of crop canopies is corrupted by wind-induced motion that violates the static-scene assumption on which NeRF training rests. The framework formalises the relationship between wind speed, exposure time, view sparsity and surface reconstruction error, and it specifies the conditions under which sparse-view NeRF can deliver phenotyping-grade geometry. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The analysis indicates that 52 clusters are required to detect a 5 percentage-point effect at 80% power under this variance structure. The contribution is a formal, testable account of when sparse-view neural radiance reconstruction is geometrically trustworthy for canopy phenotyping, together with a design-analysis protocol that quantifies the cluster counts needed to evaluate such systems in the field.