- Dr. Marco Caccamo, Professor, Dept. of Computer Science
- Dr. Kaustubh Bhalerao, Associate Professor, Dept. of Agricultural & Biological Engineering
- Or Dantsker, Graduate Student, Dept. of Aerospace Engineering
Unmanned aerial vehicle (UAV) technology is rapidly maturing and has found numerous applications including precision agriculture. However, despite their promise, the UAV industry has not been persuasive in communicating value to the grower. One of the ways in which UAVs can help a grower become more profitable is to reduce unnecessary input costs. In particular, the application of fungicides is only economically beneficial when the fungal pathogen is clearly identified and the application area is well-bounded. However, because it is expensive to continually scout the crop, most growers apply fungicides prophylactically during the growing season, as any omission of crop disease treatment can quickly lead to significant losses. Yet, if it were possible to apply the correct type of fungicide when actually required, significant savings could be realized each season, while minimizing environmental impact. In the proposed approach, the data is processed online and onboard by an artificial intelligence (AI) engine on a long-endurance UAV equipped with a low-power, high-performance computing platform. The processed data is expressed through a modeled or learned feature set. This feature set is analyzed in order to form a closed mission loop, enabling online adaptation of the data collection algorithm (e.g. its flight path). The extracted features can be shared with the user on the ground since their compact representation of the raw data requires minimal transmission bandwidth. Additionally, sharing the features with the cloud and, hence, other edge devices and users allows for distributed big data analysis, shifting the paradigm of agricultural practice. Within the context of the Center for Digital Agriculture, the proposed effort aims to create advances across multiple domains: Data, Crops & Animals, and Automation. Specifically, the proposed framework will improve the process of autonomous data collection for a variety of crop and livestock applications by closing the mission feedback loop.