Tree Biological Data Prediction Using 3D Sensing and Environmental Data

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Solution Overview

Problem

There is a lack of methods to predict biological data of trees, such as photosynthesis amount and carbon dioxide capture, in natural environments, limiting the understanding of how trees respond to environmental changes.

Innovation Solution

A biological data prediction system that collects three-dimensional and two-dimensional tree observation data, along with environmental data, using devices like stereo cameras, LiDAR, and multispectral cameras, and applies machine learning to calculate vegetation indices, growth amounts, and capture amounts of carbon dioxide and fine dust.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If laboratory or controlled environment studies are used to study tree photosynthesis and air purification, then experimental control and measurement precision are improved, but applicability to natural environments and ecological relevance deteriorate

Engineering Contradiction:

Improvebiological data measurement precisionVSAvoidapplicability to natural environments

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent introduces remote sensing technology as an intermediary between laboratory studies and natural environment applications. By using satellites and aerial sensors to collect tree data in natural settings, the system bridges the gap between controlled experimental data and real-world ecological applications, enabling both measurement precision and environmental adaptability simultaneously

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanical field measurement methods with remote sensing technology. Instead of physically measuring trees in natural environments (which is difficult and invasive), the system uses optical, thermal, and radar sensors to non-contactively capture tree data, thereby maintaining measurement precision while enabling natural environment applicability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional crop production prediction methods are used, then agricultural productivity prediction is improved, but ability to predict tree biological data such as photosynthesis and carbon dioxide capture deteriorates

Engineering Contradiction:

Improvecrop production prediction accuracyVSAvoidprediction of tree biological data

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal prediction system that can handle both traditional crop production predictions and tree biological data predictions using the same remote sensing framework. The system uses multi-spectral imaging and machine learning algorithms that work for both agricultural crops and trees, thereby achieving versatility across different biological prediction applications while maintaining productivity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent adapts prediction parameters from crop agriculture to forestry by transforming the measurement and prediction framework. It uses tree-specific parameters such as canopy structure, leaf area index, and vegetation indices derived from remote sensing data, enabling accurate prediction of tree photosynthesis and carbon dioxide capture while maintaining the productivity of the prediction system

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If comprehensive remote sensing data collection is implemented, then prediction accuracy is improved, but system complexity and data processing requirements deteriorate

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex remote sensing system into modular components: satellite-based multi-spectral imaging, aerial mobile measurement units, ground-based sensors, and centralized data processing centers. This segmentation allows each component to be optimized independently while working together to achieve high prediction accuracy without overwhelming system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces machine learning algorithms as an intermediary between raw remote sensing data and final predictions. The algorithms automatically process and interpret complex multi-source data, transforming it into meaningful predictions about tree photosynthesis, growth, and carbon dioxide capture, thereby reducing the complexity burden on human operators while maintaining high accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260105464A1Biological data prediction system for trees

Publication Date: 2026.04.16 CATALONIX INC

AI summary

The present invention relates to a biological data prediction system for trees, wherein the biological data prediction system includes: a three-dimensional tree observation data collection part configured to collect three-dimensional tree observation data in a predetermined zone; a two-dimensional tree observation data collection part configured to collect two-dimensional tree observation data in the predetermined zone; an environmental data collection part configured to collect environmental data in the predetermined zone; and a biological data prediction part configured to predict biological data of the trees from the three-dimensional tree observation data, the two-dimensional tree observation data, and the environmental data.