Automating grapevine LAI features estimation with UAV imagery and machine learning
This provides a practical tool for precision agriculture by improving LAI calculation efficiency, though it is incremental as it builds on existing machine learning and drone technologies.
The study tackled automating leaf area index (LAI) estimation for grapevines using UAV imagery and machine learning, finding that deep learning-based feature extraction outperformed traditional methods, offering a faster, non-destructive, and cost-effective solution.
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.