用视觉识别保留多样植被，让割草机主动提升花园生物多样性
Eyes on the Grass: Biodiversity-Increasing Robotic Mowing Using Deep Visual Embeddings
- 通过深度嵌入分析图像中的植物视觉差异，识别多样化区域
- 嵌入空间分散度与专家评估的生物多样性高度相关（相关系数未提，但结果显著）
- 适合城市绿化、生态修复场景，推动智能农机向环保方向演进
本文提出一种机器人割草框架，通过视觉感知与自适应决策主动提升花园生物多样性。不同于被动恢复生态的方法，该系统利用深度特征空间分析，在摄像头图像中识别并选择性保护视觉上多样的植被区域，通过停用割草刀片实现。采用在PlantNet300K数据集上预训练的ResNet50网络生成具有生态意义的嵌入表示，基于全局偏离度指标估算生物多样性，无需物种级标注。该指标驱动选择性割草算法，动态切换割草与保护模式。系统部署于改装商用割草机器人，在模拟草坪和真实花园数据集上验证。结果表明，嵌入空间的分散度与专家评估的生物多样性呈强相关性，证实了深度视觉多样性可作为生态丰富度的有效代理指标，且所提割草决策方法有效。广泛推广此类系统，将使原本生态价值低的单一草坪转变为充满活力的生物栖息地，显著提升城市生物多样性。
原文摘要 · Abstract (English)
This paper presents a robotic mowing framework that actively enhances garden biodiversity through visual perception and adaptive decision-making. Unlike passive rewilding approaches, the proposed system uses deep feature-space analysis to identify and preserve visually diverse vegetation patches in camera images by selectively deactivating the mower blades. A ResNet50 network pretrained on PlantNet300K provides ecologically meaningful embeddings, from which a global deviation metric estimates biodiversity without species-level supervision. These estimates drive a selective mowing algorithm that dynamically alternates between mowing and conservation behavior. The system was implemented on a modified commercial robotic mower and validated both in a controlled mock-up lawn and on real garden datasets. Results demonstrate a strong correlation between embedding-space dispersion and expert biodiversity assessment, confirming the feasibility of deep visual diversity as a proxy for ecological richness and the effectiveness of the proposed mowing decision approach. Widespread adoption of such systems will turn ecologically worthless, monocultural lawns into vibrant, valuable biotopes that boost urban biodiversity.
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