Global forest managers must work with governments and academic institutions to fully capitalise on the power of artificial intelligence —a game changer for transport planning, inventory management, waste reduction, and sustainability. That is according to a new report, The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics, published in the Future Transport journal.
Led by Dr Leonel Nunes, a professor of engineering at the University of Porto, the study examined 80 examples of forestry-based machine learning applications in Sweden, Uruguay, Portugal, and India, revealing that AI was especially promising for transport optimisation and managing pests and diseases:
“In Uruguay’s eucalyptus plantations for example, (our) results demonstrated that optimised planning reduced transportation costs by approximately 15% and increased the net present value by 10%, while also lowering carbon emissions by 12% through efficient routing.” Meanwhile, “the application of drones equipped with computer vision technologies has proven to be a promising tool for real-time monitoring, particularly in the context of pest and disease management.”
“The use of systems based on data obtained by unmanned aerial vehicles (UAVs) allows for the rapid identification and monitoring of the presence of insects and diseases in forest areas, integrating multispectral and visible analysis techniques to improve accuracy in early detection.”
Amongst benefits, Nunes said machine learning models can benefit inventory management by predicting product degradation, optimising rotation cycles, and aligning inventory levels with market demand. At the same time, AI systems could improve waste management by classifying raw materials before felling, discarding biomass, and using processes to improve material recovery.
Despite the proven benefits, Nunes identified several barriers slowing AI’s widespread adoption in forest supply chains. “Chief among them is the lack of high-quality, representative data,” he said. “Many forested regions, especially in developing economies, suffer from fragmented or nonexistent digital records. (And because) AI systems require large datasets to train models effectively; limited data can undermine prediction accuracy and decision reliability.”
Then, there are infrastructure limitations because AI systems rely on consistent connectivity, cloud platforms, and sensor networks, which are not always available in remote forests. “Without reliable digital infrastructure, real-time optimisation and monitoring remain theoretical rather than actionable.” And then there are human limitations, which could see between 20% and 30% of manual labour jobs in the field replaced with AI-enabled automation.
However, Nunes said many of these barriers can be addressed with technological changes: “Integrating AI with other emerging technologies presents a central opportunity to transform the sector. This technological convergence expands the individual capabilities of each solution and creates synergies that allow complex problems to be tackled more effectively,” he said. “Combining IoT (Internet of Things) sensors with AI algorithms and blockchain-based platforms can increase traceability and transparency along the value chain, fostering trust between stakeholders and facilitating certification.”
- For more information about how AI is being used make the generation of timber products, click here for Wood Central’s special feature on ‘AI Mass Timber.’
