AgriTech Projects: Precision Farming, Digital Agronomy and Farm AI

I was building the technology engine for a new agriculture venture designed to put capital and modern agronomy into the hands of 1000+ rural agripreneurs in Kenya and India: an AI-first architecture from scratch to scale to thousands of farms, data-driven digital agronomy pipelines powered by LLMs and machine learning, and predictive farming with multi-agent systems to select the right lands and crops.

I helped shape Lima Labs' computer vision products for highly accurate yield forecasting and analytics on commercial farms in Kenya. By shifting away from complex mobile dashboards to visual insights displayed on central TVs, we drastically improved adoption and helped field teams spot trends and yield shifts earlier.

I led an external R&D team that scaled a precision farming idea into a multi-product agritech SaaS serving over 10,000 farmers across Europe and Brazil. Over seven years, I managed key tech hires and engineering operations for this award-winning, VC-backed startup.

In a weekend agritech hackathon, our team built a working prototype that finds fields prone to waterlogging from Sentinel satellite imagery (NIR, SWIR1 and RED bands): U-Net segmentation of the tiles, in a web app where users select an area and export the result as GeoJSON. Eight teams entered; ours reached the final four.

I helped design a computer vision system for dairy farms that used advanced pose estimation to identify individual cows. The pipeline continuously monitors their health states and rumination periods, turning physical farm activity into structured health data.

I contributed to the product design of a leading precision farming SaaS to help optimize crop management, field mapping, and agronomic operations.