Eyes on the Grass: Biodiversity-Increasing Robotic Mowing Using Deep Visual Embeddings

TL;DR

This paper introduces a robotic mowing system that uses deep visual embeddings to identify and preserve biodiversity in lawns, actively promoting ecological richness through adaptive mowing decisions.

Contribution

It presents a novel biodiversity-aware mowing framework utilizing deep feature analysis to selectively conserve diverse vegetation patches.

Findings

Embedding-space dispersion correlates with expert biodiversity assessments.

The system effectively balances mowing and conservation behaviors.

Validated on real garden datasets with promising results.

Abstract

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.