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  1. This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops. 
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  2. Abstract

    Advancements in the use of genome‐wide markers have provided unprecedented opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost‐effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)‐based high‐throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time‐series manner. In this experiment, 233 inbred lines from the maize (Zea maysL.) diversity panel were grown in the field under different nitrogen treatments. Unmanned aerial vehicle images were collected during different plant developmental stages throughout the growing season. A workflow for extracting plot‐level images, filtering images to remove nonfoliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the workflow, about 100,000 plot‐level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield‐related traits. The genome‐wide association study was performed, resulting inn = 29 unique genomic regions associated with image extracted traits from two or more of the 12 total time points. A candidate geneZm00001d031997, a maize homolog of theArabidopsis HCF244(high chlorophyll fluorescence 244), located underneath the leading single nucleotide polymorphisms of the canopy coverage associated signals were repeatedly detected under both nitrogen conditions. The plot‐level time‐series phenotypic data and the trait‐associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

     
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