Forestry Solutions
Object Detection for Timber Sector / Case
AAI Labs completed a project aimed at modernizing the forestry sector through smart technologies. The project focused on developing advanced AI models and utilizing computer vision to increase efficiency across the timber supply chain.
Problem
Traditional forest management and timber processing are often riddled with inefficiencies such as inaccurate timber volume estimations, delayed identification of wood defects, and the lack of foresight into market demands. Manual inspections, commonly used in this sector, slow operations and increase the likelihood of human error, leading to wasted resources and increased costs. Traditional methods like measuring timber volume via water displacement are highly impractical. Similarly, while modern machines equipped with sensors can measure timber diameters for volume calculations, these methods still require substantial human input. Additionally, wood defects are typically identified visually, which can overlook key issues not visible to the naked eye. AI-based solutions, such as image-based defect detection using convolutional neural networks, offer a much faster, more efficient approach. Using such tools allows for more accurate and efficient management of forest resources, leading to optimized supply chains and more sustainable forestry practices.
Solution
We integrated AI-driven models into forest management and wood processing workflows. The models provide a more efficient, accurate, and scalable approach to managing timber resources, from estimating timber volumes to identifying wood defects and forecasting supply and demand.
Timber volume estimation:
The models can estimate timber volumes based on images of log piles and sawn timber, distinguishing between different types of wood and providing accurate measurements. This reduces reliance on traditional manual methods, minimizing the likelihood of errors.
Defect detection:
The system detects wood defects such as splits, rot, and diseases, improving the quality of the timber processed and reducing wastage. Advanced algorithms analyze images to pinpoint defects that may not be visible to the human eye.
Forest health assessment:
By analyzing satellite imagery, the AI solution identifies forest cover types, such as deciduous or coniferous, and monitors forest health. This data is used to forecast the timber yield and evaluate environmental factors like climate change impacts.
By identifying and addressing defects early in the processing chain, the system optimizes timber use, minimizing waste and maximizing the value of each log. This improves product quality and boosts market value, delivering better financial outcomes for stakeholders. Real-time, accurate data provides enhanced planning and decision-making capabilities, enabling faster responses and more effective adaptation to market shifts. Additionally, the system promotes sustainable forestry practices by monitoring forest health and assessing resource availability, reducing the risk of illegal logging and ensuring long-term environmental management.
"This is a major step forward in technological progress in the forestry sector. The product we have developed allows foresters to obtain more accurate information on timber volumes and quality, reducing the likelihood of human error and enabling them to react quickly to market changes", said Aistis Raudys, CEO of AAI Labs.
Future prospects
Our technology has potential for application across other industries. For instance, in the agriculture sector, AI-driven analysis can be utilized to monitor crop health through satellite imagery, identifying disease outbreaks early or optimizing irrigation and resource use, much like the monitoring of forest health. This helps farmers reduce waste, improve yields, and increase profits while promoting sustainable farming practices.
In the mining industry, similar AI models can be used to assess resource deposits and forecast supply and demand, helping companies plan their extraction processes more efficiently while reducing environmental impacts. Mining companies could adjust operations to prevent over-extraction and ensure the sustainability of natural resources. Furthermore, urban infrastructure management could benefit from AI models with image analysis to monitor the structural integrity of buildings and bridges, identifying early signs of degradation, or potential defects.
Client
SIA “BONO”
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