Digitizing Trees and Their Health at Scale, Leveraging Multi-spectral Satellite Imagery

Please fill out the form to watch the smart talk

Date recorded: Sept. 03, 2021

Maximizing yield from limited land is vital to sustainable agriculture and continuing to feed the world. This is especially true for large-scale plantations covering millions of hectares of land, where customized tree-level interventions are not scalable without remote sensing artificial intelligence (AI).

Vulcan AI has developed and deployed an AI-based solution that leverages multi-spectral satellite imagery to accurately determine every single tree over large areas, the health of each tree and targeted actions to enable optimal yield. This helps in ensuringnthat yield is maximized and also that input use, such as fertilizer is optimized for sustainable growth.

Join our experts from Vulcan AI to learn about the latest innovations in AI combined with agronomy-based validation, and how Vulcan AI's solution is used in crops, paper and pulp and urban forestry to drive timely insights to clients.

Presented by


Kamal Mannar

Head of Applied Intelligence I Vulcan AI

Kamal Mannar is Head of AI at Vulcan AI, an AI company that focuses on building applied intelligence solutions for Industry 4.0 in agriculture, the public sector, construction and more. Mannar holds a PhD in industrial and systems engineering from University of Wisconsin Madison and he has more than 10 patents and over 10+ peer-reviewed publications in AI and data science.

Across his 15+ years of experience in North America, Australia and ASEAN, Mannar has developed and deployed advanced analytics, AI and internet of things solutions across diverse businesses in healthcare, government, oil and gas, mining, forestry, agriculture, utilities and telecommunications. Some innovative AI applications include leveraging deep learning for areas such as precision agriculture, health-care, smart grid, prognostics and health management, AI-enabled commodity trading and counterfeit and fraud detection using GANs.