Precision Agriculture X-Lab

Precision Agriculture Innovation Challenge

The Precision Agriculture (PA) Challenge leverages visual Artificial Intelligence (AI) technology and imagery, such as multispectral, to enable farmers to remotely monitor crop variability, leading to improved farming efficiencies. Teams will engage with farmers and technology companies to explore options for innovations that evaluate vegetative indices, helping to solve some of their immediate and most pressing challenges.


An example challenge and testbed opportunity includes sexing hemp plants. Industrial hemp farmers producing high-CBD medicinal varieties are challenged to identify and remove the male plants prior to reaching their point of maturity.  This is an intensely laborious process, especially when growing on a large scale. Farmers must walk a field to distinguish between sexes and manually remove all the males; this is only successful if the farmer knows exactly what morphological characteristics to look for. Males that are not removed have the potential to pollinate hundreds of the surrounding females, thus greatly lowering potential cannabinoid production.


Applying Remote Sensing (RA) techniques combined with image analytics, we can help to assess vegetative health of the plants.  By adding geographic identification metadata, or geotagging, farmers are informed where to direct limited resources, such as labor.  These are some examples of the technologies immediately available to teams to gather data for the precision agriculture X-lab challenge. 




Head of Resource Sustainability Innovation XLab, CITRIS Foundry

Thomas Azwell is an experienced scientist with a demonstrated history of working in higher education. He is skilled in corporate sustainability, remediation, agriculture, and oceanography. Thomas is a strong research professional with a Masters in Science Education and a PhD in Environmental Science from the University of California, Berkeley.