ESTIMATING PLANT PHENOTYPIC TRAITS FROM RGB IMAGERY

Abstract

Plant Phenotyping is a set of methodologies for measuring and analyzing characteristic traits of a plant. While traditional plant phenotyping techniques are labor-intensive and destructive, modern imaging technologies have provided faster, non-invasive, and more cost-effective capabilities for plant phenotyping. Among different image-based phenotyping platforms, I focus on phenotyping with image data captured by Unmanned Aerial Vehicle (UAV) and ground vehicles. The crop plant used in my study is sorghum [Sorghum bicolor (L.) Moench]. In this thesis, I present multiple methods to estimate plot-level and plant-level plant traits from data collected by various platforms, including UAV and ground vehicles. I propose an image plant phenotyping system that provides end-to-end RGB data analysis for plant scientists. I describe a plant segmentation method using HSV color information. I introduce two methods to locate the center of the plants using Multiple Instance Learning (MIL) and Convolutional Neural Networks (CNN). I present three methods to segment individual leaves by shape-based approaches in both Cartesian coordinates and Polar coordinates. I propose a method to estimate leaf length and width for overhead leaf images. I describe a method to estimate leaf angle from data collected by a modified wheel-based sprayer with a sensor boom vehicle, Phenorover. Methods are tested and verified on image data collected by UAV and ground vehicle platforms in sorghum fields in West Lafayette, Indiana, USA. Estimated phenotypic traits include plant locations, the number of plants per plot, leaf area, canopy cover, Leaf Area Index (LAI), leaf count, leaf angle, leaf length, and leaf width.