mlsanityData to 14 Oct 2026

SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping

arXiv 2603.27519 ↗First posted 29 Mar 2026cs.CVcited by 1

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Abstract (arXiv)

Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, organs, growth stages, and field conditions. General-purpose vision foundation models offer a natural route to label efficiency, but their web-scale pretraining objectives transfer weakly to agricultural imagery, where semantics are often determined by fine organ geometry inside repetitive, texture-dominated scenes. We introduce SPROUT, a diffusion foundation model for multi-crop plant phenotyping. SPROUT learns from 2.6 million unlabeled open-field images (MCD-2.6M) using a pixel-space Diffusion Transformer, and selects transferable features with a label-free effective-rank criterion over denoising timesteps. This design shifts pretraining from crop-based invariance to structure-preserving denoising, making the representation better aligned with dense phenotyping tasks. We evaluate SPROUT across dense phenotyping tasks, including organ segmentation, crop-weed parsing, depth estimation, and counting. SPROUT consistently improves over strong web-pretrained baselines, with the largest gains on dense structural prediction, and shows favorable label and compute efficiency compared with general-purpose and crop-specific foundation models. The source code and MCD-2.6M dataset are publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 29 Mar 2026.

Builds on

33 of 48 references.

References · 33

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretrainingcited by 0

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  1. 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis
  2. 2026AgriScope: Pixel-Grounded Multimodal Understanding for Agricultural Images
  3. 2025AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification
  4. 2026From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping
  5. 2026Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping
  6. 2025Generative diffusion models for agricultural AI: plant image generation, indoor-to-outdoor translation, and expert preference alignment
  7. 2026Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
  8. 2025A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges