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Automating grapevine LAI features estimation with UAV imagery and machine learning

Muhammad Waseem Akram, Marco Vannucci, Giorgio Buttazzo, Valentina Colla, Stefano Roccella, Andrea Vannini, Giovanni Caruso, Simone Nesi, Alessandra Francini, Luca Sebastiani

TL;DR

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.

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.

Automating grapevine LAI features estimation with UAV imagery and machine learning

TL;DR

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.

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.

Paper Structure

This paper contains 14 sections, 10 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Materials and Method
  3. Data collection
  4. Methodology
  5. Green Area Features Extraction using an Edge Detection
  6. Feature vocabulary development
  7. Pre-trained model to extract features from images
  8. Machine Learning Models
  9. Linear Regression (LR)
  10. Support Vector Machines (SVM)
  11. Random Forest (RF)
  12. Model Implementation and Parameter Tuning
  13. Results and Discussion
  14. Conclusion and Future Work

Figures (3)

  • Figure 1: Examples a and b are annotated images with bounding boxes showing individual plants identified during the annotation process. Each bounding box corresponds to a plant subjected to LAI measurement.
  • Figure 2: Examples a,b,c and d are images showing individual plants
  • Figure 3: Overview of the Leaf Area Estimation process using drone images and Machine Learning approaches