Northwest Nazarene University

Northwest Nazarene University - Aerial Idaho

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Interview with Duke Bulanon (Northwest Nazarene University, United States)

Current research involves the development of remote sensing technologies for orchard management which includes aerial and ground imaging, and control of mobile utility robots. The research projects are funded by the Specialty Crop Research Block Grant by the United States Department of Agriculture through the state of Idaho and NASA Idaho Space Grant Consortium.

Dr. Duke M Bulanon earned his BS Mechanical Engineering degree from the University of San Carlos (Philippines) in 1995. He earned his MS and PhD in Agricultural Engineering from Iwate University (Japan) in 1999 and 2003, respectively. He was a postdoctoral fellow in Hokkaido University (Japan) doing research in robotic fruit harvesting, for apples, in 2003 through the Japan Society for the Promotion of Science Fellowship. He continued his research in robotic fruit harvesting, for oranges, as a Research Associate in the University of Florida in 2006. Currently, he is an Associate Professor of Physics and Engineering Department at Northwest Nazarene University (Nampa, Idaho). Dr Bulanon is a licensed Professional Engineer in the State of Idaho.

This video was recorded at FTC 2017 -
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Abstract: Precision agriculture involves observing the crop production process and applying appropriate actions to improve production efficiency. In this paper, a smart vision system is developed to monitor specialty crops which include fruits and vegetables. The smart vision system is composed of the image acquisition module and the image processing element. The image acquisition module is a modified point and shoot camera that could detect both visible and near-infrared wavebands, while the image processing element takes the multispectral image as an input and processes the images using a customized image processing algorithm for crop assessment. The smart vision system was tested using an experimental apple orchard, a commercial onion field, and a peach orchard. Results showed that the smart vision system was able to differentiate different watering input in the apple orchard, recognize the blossoms in the peach orchard, and detect the variation in the onion field.