Imaging Science Ph.D. Defense: Amir Hassanzadeh
Ph.D. Dissertation Defense
On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop
Amir Hassanzadeh
Imaging Science Ph.D. Candidate
Chester F. Carlson Center for Imaging Science, RIT
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This seminar may be attended in person in 3215 Carlson Building or online via Zoom.
A site-specific approach using Hyperspectral UAS and biophysical data to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest of Snap Beans.
Abstract:
Snap Bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by taking limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers as well as providing them with exact detail to when and where to harvest while forecasting yield. In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there has been immense advances in the field of yield and harvest modelling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system.
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
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Open to the Public
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