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<p>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 review of two-dimensional (2D) and three-dimensional (3D) synthetic data generation (SDG) pipelines in agriculture. We outline 2D pipelines ranging from classical augmentation and compositing to learning-based generative models, and we review 3D approaches spanning procedural plant modeling, photorealistic rendering engines, customized simulation platforms, and reconstruction-based assets. This review focuses on 2D and 3D SDG for agricultural vision tasks (image acquisition, object detection, semantic and instance segmentation, and classification), including weed and disease management, phenotyping, yield management, precision livestock farming, aquaculture, and fruit management. Beyond cataloguing methods, we adopt a deployment-oriented perspective and present task-driven design rules: select SDG according to the primary bottleneck (class imbalance, occlusion, or domain shift); favor a "3D geometry first, 2D stylization later" workflow when geometric fidelity and visual realism must both be preserved; combine domain randomization or translation with limited target-domain fine-tuning; and align method choice with available expertise and computing resources. We also synthesize challenges related to realism and diversity, generalization and sim-to-real transfer, and ethical and legal considerations. For evaluation, we compile common practices, including expert plausibility checks, quantitative image metrics, and controlled comparisons on identical real-world test sets, complemented by ablation studies and statistical tests. Ultimately, the review integrates methodological, empirical, and deployment perspectives into a practical roadmap for selecting, building, and validating SDG pipelines that remain agronomically credible and transferable to real agricultural environments.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Weilong He,
  • Xingjian Li,
  • Yuxi Chen,
  • Prem Raj,
  • Charles Fleming,
  • Dong Chen,
  • Manoj Karkee,
  • Lirong Xiang

摘要

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 review of two-dimensional (2D) and three-dimensional (3D) synthetic data generation (SDG) pipelines in agriculture. We outline 2D pipelines ranging from classical augmentation and compositing to learning-based generative models, and we review 3D approaches spanning procedural plant modeling, photorealistic rendering engines, customized simulation platforms, and reconstruction-based assets. This review focuses on 2D and 3D SDG for agricultural vision tasks (image acquisition, object detection, semantic and instance segmentation, and classification), including weed and disease management, phenotyping, yield management, precision livestock farming, aquaculture, and fruit management. Beyond cataloguing methods, we adopt a deployment-oriented perspective and present task-driven design rules: select SDG according to the primary bottleneck (class imbalance, occlusion, or domain shift); favor a "3D geometry first, 2D stylization later" workflow when geometric fidelity and visual realism must both be preserved; combine domain randomization or translation with limited target-domain fine-tuning; and align method choice with available expertise and computing resources. We also synthesize challenges related to realism and diversity, generalization and sim-to-real transfer, and ethical and legal considerations. For evaluation, we compile common practices, including expert plausibility checks, quantitative image metrics, and controlled comparisons on identical real-world test sets, complemented by ablation studies and statistical tests. Ultimately, the review integrates methodological, empirical, and deployment perspectives into a practical roadmap for selecting, building, and validating SDG pipelines that remain agronomically credible and transferable to real agricultural environments.