From 2D image synthesis to 3D scene generation: a comprehensive review of synthetic data for agricultural vision

Despite rapid digitalization, agricultural computer vision still faces persistent data bottlenecks. The collection and annotation of field images are constrained by seasonality and biological variability, require domain expertise, must adapt to the perception and navigation conditions of ground robots, and are further limited by privacy and data-sharing concerns. Together, these factors restrict the scale, diversity, and transferability of real-world datasets. This paper provides a systematic re

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ethics.ai (31 July 2026), “From 2D image synthesis to 3D scene generation: a comprehensive review of synthetic data for agricultural vision,” evidence record 15400, https://ethics.ai/record/15400 (originally published by Artificial Intelligence Review).

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