Image-based Plant Phenotyping Using Machine Learning

Phenotypic data is of crucial importance for plant breeding in estimating a plant's biomass. Traits such as leaf area and plant height are known to be correlated with biomass. Image analysis and computer vision methods can automate data analysis for high-throughput phenotyping. Many methods have been proposed for plant phenotyping in controlled environments such as greenhouses. In this thesis, we present multiple methods to estimate traits of the plant crop sorghum from images acquired from UAV and field-based sensors. We describe machine learning techniques to extract the plots of a crop field, a method for leaf counting from low-resolution images, and a statistical model that uses prior information about the field structure to estimate the center of each plant. We also develop a new loss function to train Convolutional Neural Networks (CNNs) to count and locate objects of any type and use it to estimate plant centers. Our methods are evaluated with ground truth of sorghum fields and publicly available datasets and are shown to outperform the state of the art in generic object detection and domain-specific tasks.

This thesis also examines the use of crowdsourcing information in video analytics. The large number of cameras deployed for public safety surveillance systems requires intelligent processing capable of automatically analyzing video in real time. We incorporate crowdsourcing in an online basis to improve a crowdflow estimation method. We present various approaches to characterize this uncertainty and to aggregate crowdsourcing results. Our techniques are evaluated using publicly available datasets.