Automating grapevine LAI features estimation with UAV imagery and machine learning

TL;DR

This paper presents a machine learning approach using UAV imagery to automate grapevine leaf area index estimation, improving speed, accuracy, and cost-effectiveness over traditional methods.

Contribution

It introduces a novel combination of drone imagery and machine learning, including deep learning feature extraction, for non-destructive LAI estimation in vineyards.

Findings

Deep learning features outperform traditional methods in LAI prediction

The approach is faster and more cost-effective than conventional techniques

Enhanced precision in grapevine health assessment

Abstract

The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices.