NIFA Proposal Grant Awarded
The proposal submitted to the National Institute of Food and Agriculture (NIFA) for the Specialty Crop Research Initiative (SCRI): "Development of a UAV platform-based multi-sensor system for early detection and monitoring of powdery and downy mildew in cucurbit crops" was awarded the grant.
Prof. Fiorentini is leading the effort and will serve as Program Director for the project. The project will be run in collaboration with Michigan State University and North Carolina State University. In particular, the six PIs championing the effort are going to be Prof. Lisa Fiorentini, ECE - The Ohio State University; Prof. Jim Jasinski, Extensions - The Ohio State University; Prof. Mary Hausbeck, Plant, Soil and Microbial Sciences - Michigan State University; Ben Phillips, Extensions - Michigan State University; Inga Meadows, Extensions - North Carolina State University; Lina Quesada, Plant Pathology - North Carolina State University. The OSU Team will include Prof. John Fulton, Food, Agricultural and Biological Engineering, Prof. Sally Miller, Plant Pathology and Prof. Wladimiro Villarroel, ECE.
Cucurbit Downy Mildew (DM) and Powdery Mildew (PM) are some of the most important diseases of cucurbits worldwide, causing severe reductions in yield and loss of fruit quality. In addition to employing host plant resistance, fungicide applications are used for crop protection, and initiated when those diseases have been detected in a field or neighboring county or state. Due to the virulence of these pathogens, scouting and treatment is essential to reduce marketable losses. Traditional scouting requires walking fields and manually inspecting plants for symptoms and signs of infection. However, this approach is very labor intensive, particularly on large-scale farms, and relies on the experience of the scout to be able to recognize signs of those diseases.
The goal of this project is to design a UAV platform that uses a sensor array to detect and pinpoint signs of DM/PM diseases on cucumber and pumpkin crops. The use of this technology will have several advantages. First, different sensors (RGB, IR, Multi-Spectral, Spore Trapping, etc.) will allow for early disease detection, possibly even before they are noticeable to human eyes. Secondly, the UAV will require minimal human supervision, and be able to scout crops more frequently and thoroughly than before. Lastly, after initial disease detection, the UAV will remain useful by monitoring crop health and helping to evaluate fungicide efficacy and optimize sprayer operation and coverage. The proposed technology could also be adapted in the future to identify and quantify damage caused by diseases, insects, and weeds on different crops.