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Updated: Nov 21, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Explainable identification and mapping of trees using UAV RGB image and deep learning.

Masanori Onishi¹, Takeshi Ise²

¹Graduate School of Agriculture, Kyoto University, Kyoto, Japan. onishi.masanori.25e@st.kyoto-u.ac.jp.

Scientific Reports

|January 14, 2021

Summary

This study introduces a cost-effective machine vision system using drone-captured images and a convolutional neural network (CNN) for tree identification and mapping. The system accurately classifies tree species, aiding forest management for small-scale operations.

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Area of Science:

Forestry
Computer Vision
Remote Sensing

Background:

Accurate tree identification and mapping are crucial for forest management.
Existing methods using airborne and hyperspectral sensors are accurate but costly for small-scale managers.
There is a need for cost-effective, high-accuracy tree identification solutions.

Purpose of the Study:

To develop a machine vision system for tree identification and mapping using affordable Red-Green-Blue (RGB) imagery from unmanned aerial vehicles (UAVs).
To leverage convolutional neural networks (CNNs) for accurate tree classification based on visual and spatial data.
To provide a practical tool for small-scale forest managers.

Main Methods:

Constructed a machine vision system integrating UAV-based RGB imagery and CNNs.
Calculated slope from a 3D model derived from UAV data.
Segmented forest images into individual tree crowns using color, 3D information, and slope data.
Applied object-based CNN classification to segmented tree crown images.

Main Results:

Successfully classified seven tree classes, including multiple tree species, with over 90% accuracy.
Guided gradient-weighted class activation mapping (Guided Grad-CAM) revealed classification based on tree shape and leaf contrast.
The system demonstrated effectiveness in distinguishing trees with similar colors.

Conclusions:

The developed UAV-based RGB and CNN system offers a cost-effective solution for tree identification and mapping.
The system's ability to classify based on shape and leaf contrast enhances its utility for diverse forest environments.
This approach provides a valuable tool for improving forest management practices, particularly for small-scale operations.