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Plant Segmentation by Supervised Machine Learning Methods and Phenotypic Trait Extraction of Soybean Plants Using Deep Convolutional Neural Networks with Transfer Learning
High-throughput phenotyping systems provide abundant data for statistical analysis through plant imaging. Before usable data can be obtained, image processing must take place. My first paper proposes the use of supervised learning methods to segment plants from background in such images and compares them to commonly used thresholding methods. As obtaining accurate training data is a major obstacle to using supervised learning methods for segmentation, a novel approach to producing accurate labels is proposed. It is demonstrated that with careful selection of training data through such an approach, supervised learning methods, and neural networks in particular, can outperform thresholding methods at segmentation. High-throughput plant phenotyping systems capable of producing large numbers of images have been constructed in recent years. In order for statistical analysis of plant traits to be possible, image processing must take place. My second paper considers the extraction of plant trait data from soybean images taken in the University of Nebraska-Lincoln Greenhouse Innovation Center. Convolutional neural networks (CNNs) are trained to predict measurements such as height, width, and size of the plants. It is demonstrated that the CNNs efficiently and accurately extract the trait measurements from the images. The superiority of a CNN-based trait extraction approach to an image segmentation-based approach is also discussed.
Adams, Jason R, "Plant Segmentation by Supervised Machine Learning Methods and Phenotypic Trait Extraction of Soybean Plants Using Deep Convolutional Neural Networks with Transfer Learning" (2020). ETD collection for University of Nebraska - Lincoln. AAI27739603.