# PlantShade: Predicting Plant Shadows for Lighting-Aware Robotic Agricultural Operation

arXiv: https://arxiv.org/abs/2609.21059 · HTML: https://mlsanity.com/paper/2609.21059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [R-LPIPS: An Adversarially Robust Perceptual Similarity Metric](https://mlsanity.com/paper/2307.15157/) (2023)
- [Shadow Generation for Composite Image Using Diffusion model](https://mlsanity.com/paper/2403.15234/) (2024)
- [DeepShade: Enable Shade Simulation by Text-conditioned Image Generation](https://mlsanity.com/paper/2507.12103/) (2025)
- [Optimal Energy Management in Indoor Farming Using Lighting Flexibility and Intelligent Model Predictive Control](https://mlsanity.com/paper/2510.03686/) (2025)

## Abstract (arXiv)

Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent advances in frontier artificial intelligence have enabled scientific agriculture with strong potential to improve crop productivity. In this paper, we identify the importance and inherent complexity of plant shade simulation, as shading is a critical factor influencing plant growth. To advance this field and promote broader societal benefits, we focus on two main contributions. First, we introduce a comprehensive plant growth and shade dataset covering four plant species, including soybean, tomato, sugarbeet, and strawberry. The dataset includes top-down viewpoints with a supplementary light along a circular trajectory, casting dynamic shadows across multiple growth stages and diverse observation complexities. Second, we propose generative shade simulation based on diffusion models, enabling realistic shade generation for unseen plants and supporting downstream robotic tasks such as perception, lighting control, and view planning. The model incorporates temporal conditioning to facilitate flexible shade simulation across different time stages. We conduct both quantitative and qualitative evaluations to assess model performance. This work provides a foundational study for plant-aware shade modeling and has meaningful implications for broader agricultural and robotic applications.