TasselGAN:An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data
作者机构：School of Computing and Electrical EngineeringIndian Institute of TechnologyMandiIndia
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2020年第2卷第1期
页 面：210-224页
核心收录：
基 金：This work was supported by the Ramalingaswami Re-entry Fellowship awarded by the Department of Biotechnology,Government of India(grant number IITM/DBT-RF/SS/205) This study was partially funded by the Ucchatar Avishkar Yojana Scheme by the Ministry of Human Resource Devel-opment,Government of India under the project:Design of advanced big data analytics in CygNet management system for large telecom network(grant number IITM/MHRD(UAY)/AD/115)
主 题：qualitative typically traits
摘 要：Machine learning-based plant phenotyping systems have enabled high-throughput,non-destructive measurements of plant *** such as object detection,segmentation,and localization of plant traits in images taken in field conditions need the machine learning models to be developed on training datasets that contain plant traits amidst varying backgrounds and environmental ***,the datasets available for phenotyping are typically limited in variety and mostly consist of lab-based images in controlled ***,we present a new method called TasselGAN,using a variant of a deep convolutional generative adversarial network,to synthetically generate images of maize tassels against sky *** foreground tassel images and background sky images are generated separately and merged together to form artificial field-based maize tassel data to aid the training of machine learning models,where there is a paucity of field-based *** effectiveness of the proposed method is demonstrated using quantitative and perceptual qualitative experiments.
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