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Kevin Lang
CEO & President @ Agerpoint
Kevin Lang is the CEO and President of Agerpoint, a company specializing in spatial intelligence for agriculture and natural environments. Under his leadership, Agerpoint aims to empower sustainable food systems and enhance environmental protection through advanced monitoring of plant health, growth, yield, and carbon sequestration potential.12
Lang has a substantial background in technology and management. Before joining Agerpoint, he served as the General Manager of Agriculture at PrecisionHawk, a commercial drone technology provider, and was a co-founder of the geospatial startup spadeGEO. His experience also includes roles at Deloitte Consulting and the World Economic Forum, and he began his career in product engineering at John Deere.23
He holds a Bachelor of Science in Mechanical Engineering from Clemson University and an MBA from Wake Forest University.12
Highlights
Thrilled to have contributed to the World Economic Forum's #emergingtech23 report, out today.
Included in the Top 10 are Wearable Plant Sensors: Revolutionizing agricultural data collection to feed the world. Such technologies to improve the accuracy and accessibility of plant-level crop and tree data carry applications across the food chain, with utility from R&D to production.
While there is much work to do, the future is bright as developments in sensor hardware, AI, connectivity and remote sensing techniques continue to make their way into the fields and forests that sustain us.
Read the full report here: wef.ch/top10tech23
Was pleased to contribute Agerpoint's perspective for the report released today by McKinsey & Company and the World Economic Forum titled "Space Applications in Agriculture: Enhancing Food and Water Security, Improving Climate Action".
There's been incredible progress with earth observation satellite data for agriculture and nature, but spaceborne models are only as good as the data used for training and validation. Glad to see the report acknowledge the need for accessible ground truth:
"...getting [satellite] model accuracy to reliable levels requires high investment of human resources, as data gathered manually on the ground is required to train analytics models and validate what the images predict...ground truthing data must include a comprehensive set of variables such as crop type, the observed event (e.g. planting date, weed or disease presence) and eventual yield – all tagged to the location where the observation was made and compared to imagery produced in the same period."
