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Development of AI-Powered Forest Management System for Komatsu

dac.digital

Agriculture

Manufacturing

Construction

Challenges in Traditional Forest Management

Komatsu Forest faces inefficiencies and inaccuracies in traditional forest mapping and harvesting planning methods. These methods are slow, labor-intensive, and prone to errors, hindering optimal resource allocation, increasing operational costs, and potentially impacting environmental sustainability. The lack of a reliable, high-resolution dataset further complicates the development of effective harvesting strategies.

About the Client

Komatsu Forest is a leading provider of sustainable forestry solutions, leveraging advanced technology to optimize harvesting operations and minimize environmental impact.

Project Goals

Develop an AI-powered system for accurate tree and obstacle recognition in forest environments.
Enable optimized route planning for heavy machinery to improve harvesting efficiency.
Facilitate precise Diameter at Breast Height (DBH) estimation for improved timber quality assessment.
Enhance environmental protection by identifying and avoiding sensitive areas such as protected species habitats.
Reduce the environmental impact of harvesting operations by minimizing damage to terrain and vegetation.

System Functionality

Automated tree and obstacle detection using computer vision.
Geolocated identification of trees and obstacles.
Diameter at Breast Height (DBH) estimation for individual trees.
Terrain mapping and analysis.
Route optimization for heavy machinery, considering tree and obstacle locations.
Identification of protected areas (e.g., anthills, graves).

Preferred Technologies

LiDAR data processing

RGB image processing

Semantic Segmentation (e.g., SAM)

Machine Learning (ML) algorithms

Drone technology

Cloud-based data storage and processing

Required Integrations

GIS (Geographic Information System) platforms for map visualization and data management
Heavy machinery navigation systems for route planning

Non-Functional Requirements

High accuracy and reliability of object detection and DBH estimation.
Scalability to handle large forest datasets.
Real-time processing capabilities for dynamic operations.
Data security and privacy.
Robustness to varying environmental conditions (e.g., lighting, weather).

Expected Business Impact

This AI-powered forest management system is expected to significantly improve Komatsu Forest's operational efficiency, reduce costs, enhance environmental sustainability, and provide more accurate and accessible forest maps. Improved route planning will reduce fuel consumption and equipment wear and tear. Accurate DBH estimation will improve timber quality and maximize harvesting volume. Proactive identification of sensitive areas will minimize environmental damage and enhance the company's reputation for responsible forestry practices.