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SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods

Plant Phenomics 2022年第1期4卷 26-42页

作者： Mario Serouart Simon Madec Etienne David Kaaviya Velumani raul lopezlozano Marie Weiss Frederic Baret Arvalis Institutduvegetal228routedel’aerodrome-CS4050984914 Avignon Cedex 9France INRAE Avignon UniversiteUMREMMAHUMTCAPTE228routedel’aerodrome-CS4050984914 Avignon Cedex 9France CIRAD UMRTETISF-34398 MontpellierFrance Hiphen SAS 228routedel’aerodrome-CS4050984914 Avignon Cedex 9France

Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of *** have developed the SegVeg approach for semantic segmentation of RGB images into three classes(background,green,and senescent vegetation).This is achieved in two steps:A U-net model is first trained on a very large dataset to separate whole vegetation from *** green and senescent vegetation pixels are then separated using SVM,a shallow machine learning technique,trained over a selection of pixels extracted from *** performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth *** show that the SegVeg approach allows to segment accurately the three ***,some confusion is observed mainly between the background and senescent vegetation,particularly over the dark and bright regions of the *** U-net model achieves similar performances,with slight degradation over the green vegetation:the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of *** use of the components of several color spaces allows to better classify the vegetation pixels into green and ***,the models are used to predict the fraction of three classes over whole images or regularly spaced *** show that green fraction is very well estimated(R^(2)=0.94)by the SegVeg model,while the senescent and background fractions show slightly degraded performances(R^(2)=0.70 and 0.73,respectively)with a mean 95%confidence error interval of 2.7%and 2.1%for the senescent vegetation and background,versus 1%for green *** have made SegVeg publicly available as a ready-to-use script and model,along with the entire annotated grid-pixels *** thus hope to render segmentation acc

关键词： Deep offering render

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