# In-field high throughput grapevine phenotyping with a consumer-grade depth camera

arXiv: https://arxiv.org/abs/2104.06945 · HTML: https://mlsanity.com/paper/2104.06945/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Deep-Plant: Plant Identification with convolutional neural networks](https://mlsanity.com/paper/1506.08425/) (2015)
- [Terrain assessment for precision agriculture using vehicle dynamic modelling](https://mlsanity.com/paper/2104.06326/) (2021)
- [Ambient awareness for agricultural robotic vehicles](https://mlsanity.com/paper/2104.05270/) (2021)

## Built on by (most cited first, 3 of 3)

- [End-to-end deep learning for directly estimating grape yield from ground-based imagery](https://mlsanity.com/paper/2208.02394/) (2022)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)
- [A Low-Cost Photogrammetry System for 3D Plant Modeling and Phenotyping](https://mlsanity.com/paper/2504.16840/) (2025)

## Abstract (arXiv)

Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted onboard an agricultural vehicle.