Comprehensive Exam - Maisha Maliha

Maisha Maliha – Data Science

Zoom Link
CCP Conference Room 368

Title: Machine Learning and plant species identification from remote sensing data.

Abstract:

Plants are the backbone of maintaining the earth’s ecosystem’s balance. All living things in the world primarily obtain oxygen from plants, which also play a critical role in slowing global warming. Affluent Information on plant species and ecosystems is required for the sustainable management and conservation of nature. Though advanced remote sensing technologies have opened the path to high-resolution images for plant species, most of the past research was focused on site/landscape-specific applications. Also, Processing the Remote Sensing data with traditional approaches is time-consuming and requires extensive human supervision. The need to process advanced digital images and achieve precise predictions inspires the adoption of machine learning techniques in plant research. In this study, we studied research articles that have applied Machine Learning techniques to identify plant species from RS data. Our survey results reveal encouraging trends in recent research’ use of CNN and machine learning methods. Also, a number of studies have shown that transfer learning has the ability to improve categorization prediction. Also, the current gaps in machine learning’s applicability to detecting plant species are reviewed, and some open questions are provided.

Committee:

Dr. Trevor Caughlin (Chair), Dr. Edoardo Serra, Dr. Megan Cattau, Dr. Julia Oxford