# Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey

arXiv: https://arxiv.org/abs/2006.11391 · HTML: https://mlsanity.com/paper/2006.11391/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision](https://mlsanity.com/paper/2212.01506/) (2022)
- [Visual based Tomato Size Measurement System for an Indoor Farming Environment](https://mlsanity.com/paper/2304.06177/) (2023)
- [ViewSparsifier: Killing Redundancy in Multi-View Plant Phenotyping](https://mlsanity.com/paper/2509.08550/) (2025)
- [Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms](https://mlsanity.com/paper/2408.06356/) (2024)
- [Ortho-Fuse: Orthomosaic Generation for Sparse High-Resolution Crop Health Maps Through Intermediate Optical Flow Estimation](https://mlsanity.com/paper/2510.10360/) (2025)
- [BoltNet: An Ultra-Lightweight Convolutional Network for On-Device Plant Species Identification](https://mlsanity.com/paper/2608.11844/) (2026)

## Abstract (arXiv)

In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.