# AlphaGarden: Learning to Autonomously Tend a Polyculture Garden

arXiv: https://arxiv.org/abs/2111.06014 · HTML: https://mlsanity.com/paper/2111.06014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TrimBot2020: an outdoor robot for automatic gardening](https://mlsanity.com/paper/1804.01792/) (2018)
- [Unsupervised Domain Adaptation For Plant Organ Counting](https://mlsanity.com/paper/2009.01081/) (2020)

## Built on by (most cited first, 2 of 2)

- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [Robots in the Garden: Artificial Intelligence and Adaptive Landscapes](https://mlsanity.com/paper/2305.13019/) (2023)

## Abstract (arXiv)

This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1.5m x 3.0m physical testbed. AlphaGarden uses an overhead camera and sensors to track the plant distribution and soil moisture. We model individual plant growth and interplant dynamics to train a policy that chooses actions to maximize leaf coverage and diversity. For autonomous pruning, AlphaGarden uses two custom-designed pruning tools and a trained neural network to detect prune points. We present results for four 60-day garden cycles. Results suggest AlphaGarden can autonomously achieve 0.96 normalized diversity with pruning shears while maintaining an average canopy coverage of 0.86 during the peak of the cycle. Code, datasets, and supplemental material can be found at https://github.com/BerkeleyAutomation/AlphaGarden.