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

TL;DR

This survey reviews recent advancements in deep learning techniques for plant phenotyping, highlighting their role in improving crop monitoring and precision agriculture to meet global food demands.

Contribution

It provides a comprehensive overview of the state-of-the-art deep learning methods applied to plant phenotyping in agriculture.

Findings

Deep learning enables more accurate plant phenotyping tasks.

Enhanced crop monitoring improves precision agriculture practices.

Deep learning techniques are increasingly integrated into agricultural workflows.

Abstract

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.