SBIR Phase I: TerraSentia: Ultra-compact, Autonomous, Teachable Under-canopy Phenotyping Robot for Plant Breeders and Crop Scientists

Broader Impacts: The broader impact of this Small Business Innovation Research (SBIR) project include improving food security, while at the same time enhancing the economic viability and environmental sustainability of large-scale production agriculture. In order to improve crop varieties, agricultural production, and sustainability of farming, there is an urgent need for better technologies to acquire under-canopy plant trait and health data. Examples of high-value under-canopy data include emergence, stem width, corn ear height, plant life-cycle events like flowering and fruiting, and symptoms of pathogens, diseases, and nutrient deficiency. Because these data cannot be obtained by aerial imaging, under-canopy data collection has dramatically greater actionability and value compared to aerial data. However no cost-effective, scalable ways of collecting this data are currently available. In fact, the state of the art is manual data collection by crop scientists (and their students or interns), agronomists, crop-scouts or farmers - an extremely labor intensive, and therefore expensive way of collecting this highly valuable data. Our work will greatly enhance the availability of under-canopy data from field crops. The commercial value of the field data for crop breeding is in excess of $50 Million/year for breeding major row-crops in the US. Intellectual Merits: This SBIR Phase I project will demonstrate the technical feasibility of autonomously collecting under-canopy data from field crops using TerraSentia, our low-cost ground robot. In preliminary work, we have built the robot hardware, demonstrated its ability to collect high-value plant data from row-crop fields, and analyze it to generate plant-trait information. In the proposed work, we will enable and demonstrate the ability of TerraSentia to collect data autonomously throughout the season. We will demonstrate the technical feasibility of fusing information from low-cost LIDAR, GPS, and vision. We will also demonstrate the feasibility using real-time control algorithms to adapt camera perspective and robot path in order to obtain the highest quality information from the complex and dynamic under- canopy field environments. These high-risk innovations will together enable long-term deployment of TerraSentia for effective data collection and phenotyping, benefiting crop scientists and agricultural product development professionals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.