1,720,963 research outputs found
High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants
Get PDFHigh-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.This article is published as Guo Xingche Qiu Yumou Nettleton Dan Schnable Patrick S. High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants. Plant Phenomics. 2023:5;0052. DOI:10.34133/plantphenomics.0052.Copyright © 2023 Xingche Guo et al. Distributed under a Creative Commons Attribution License (CC BY 4.0)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Statistical methods for environment field trials, high-dimensional functional data, and image-based high-throughput phenotyping
Get PDFThis dissertation is composed of three research projects focused on developing statistical methodologies, theories, and algorithms for solving large-scale real-world problems with complex features.
The first project in chapter 2 deals with massive datasets on corn phenotypes and genotypes. We propose a novel hierarchical spatial Finlay-Wilkinson model for analyzing yield data and characterizing genotype-by-environment interactions from multi-environment field trials. The key ingredients in our hierarchical framework are (1) kinship information on the relatedness among genotypes from DNA sequence data, (2) spatial correlation among plot effects within fields and (3) environmental covariates obtained from weather stations. Together these ingredients enhance estimation of genotypic and environmental effects and reduce bias in estimating adaptability of the genotypes. Keeping practical application in mind, we develop a fast MCMC algorithm that allows us to sample from the posterior. Using a publicly available data from the Genomes to Fields initiative, we demonstrate that our method improves yield prediction over existing methods and permits yield predictions for new genotypes in new environments.
The second project in chapter 3 involves analyzing high-dimensional functional data.
we explore functional linear regression by focusing on the large-scale scenario that scalar response is associated with potentially an ultra-large number of functional predictors in the setting of a reproducing kernel Hilbert space (RKHS) framework. We propose a functional elastic-net model and introduce the Karush-Kuhn-Tucker (KKT) conditions in function spaces. By the functional KKT conditions, we show the unique solution of functional elastic-net exists. We provide sufficient conditions for establishing variable selection consistency and prediction consistency. An computational algorithm is also developed to solve the functional elastic-net problem efficiently. The performance of the proposed method is evaluated by simulation studies in various high-dimensional settings.
The third project in chapter 4 deals with image-based high-throughput phenotyping data. Specifically, the goal is to extract plant heights from an image-based field phenotyping system. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant heights, and functional curve fitting of the extracted heights. This approach is efficient and does not require human intervention.
Our results show that KAT4IA is able to accurately estimate plant heights during which the plants in the first row do not overlap with plants in the background.
In chapter 5, we describe a sequential CNN pipeline that uses plant images in early growth stages to construct training data for separating foreground and background plant pixels for late stages of plant growth. This pipeline, together with KAT4IA, provides accurate plant height estimations during the entire plant growth period
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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