# Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation

arXiv: https://arxiv.org/abs/2305.09418 · HTML: https://mlsanity.com/paper/2305.09418/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 9 of 9)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything](https://mlsanity.com/paper/2304.06022/) (2023)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)
- [Deep learning universal crater detection using Segment Anything Model (SAM)](https://mlsanity.com/paper/2304.07764/) (2023)
- [Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants](https://mlsanity.com/paper/1903.08583/) (2019)
- [LeafMask: Towards Greater Accuracy on Leaf Segmentation](https://mlsanity.com/paper/2108.03568/) (2021)

## Built on by (most cited first, 12 of 12)

- [ASAM: Boosting Segment Anything Model with Adversarial Tuning](https://mlsanity.com/paper/2405.00256/) (2024)
- [Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation](https://mlsanity.com/paper/2507.09577/) (2025)
- [Learn from Foundation Model: Fruit Detection Model without Manual Annotation](https://mlsanity.com/paper/2411.16196/) (2024)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)
- [MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models](https://mlsanity.com/paper/2407.04711/) (2024)
- [PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping](https://mlsanity.com/paper/2504.19818/) (2025)
- [Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO](https://mlsanity.com/paper/2406.19057/) (2024)
- [Zero-shot Hierarchical Plant Segmentation via Foundation Segmentation Models and Text-to-image Attention](https://mlsanity.com/paper/2509.09116/) (2025)
- [CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model](https://mlsanity.com/paper/2605.16901/) (2026)
- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)
- [Curvature-aware 3D length estimation of greenhouse cucumbers using RGB-D imaging and cubic spline arc-length integration](https://mlsanity.com/paper/2606.22439/) (2026)

## Abstract (arXiv)

Segment Anything Model (SAM) is a new foundation model that can be used as a zero-shot object segmentation method with the use of either guide prompts such as bounding boxes, polygons, or points. Alternatively, additional post processing steps can be used to identify objects of interest after segmenting everything in an image. Here we present a method using segment anything together with a series of post processing steps to segment potato leaves, called Leaf Only SAM. The advantage of this proposed method is that it does not require any training data to produce its results so has many applications across the field of plant phenotyping where there is limited high quality annotated data available. We compare the performance of Leaf Only SAM to a Mask R-CNN model which has been fine-tuned on our small novel potato leaf dataset. On the evaluation dataset, Leaf Only SAM finds an average recall of 63.2 and an average precision of 60.3, compared to recall of 78.7 and precision of 74.7 for Mask R-CNN. Leaf Only SAM does not perform better than the fine-tuned Mask R-CNN model on our data, but the SAM based model does not require any extra training or annotation of our new dataset. This shows there is potential to use SAM as a zero-shot classifier with the addition of post processing steps.