Classification of Soybean Pubescence from Multispectral Aerial Imagery  

作　　者：Robert W.Bruce Istvan Rajcan John Sulik 

机构地区：[1]Department of Plant Agriculture,University of Guelph,Guelph,ON,Canada

出　　处：《Plant Phenomics》2021年第1期156-166,共11页植物表型组学（英文）

基　　金：Canada First Research Excellence Fund,Food from Thought:Agricultural Systems for a Healthy Planet(CFREF-2015-00004);For the additional funding,thanks are due to Natural Sciences and Engineering Research Council(NSERC)Collaborative Research and Development Grant(CRD)#CRDPJ 513541-17,which is cofunded by CanGro Genetics Inc.and Huron Commodities Inc.

摘　　要：The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration.Currently,soybean pubescence is classified visually,which is a labor-intensive and time-consuming activity.Additionally,the three classes of phenotypes(tawny,light tawny,and gray)may be difficult to visually distinguish,especially the light tawny class where misclassification with tawny frequently occurs.The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow,develop a set of indices for distinguishing pubescence classes,and test a machine learning(ML)classification approach.A principal component analysis(PCA)on hyperspectral soybean plot data identified clusters related to pubescence classes,while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability.Aerial images from 2018,2019,and 2020 were analyzed in this study.A 60-plot test(2019)of genotypes with known pubescence was used as reference data,while whole-field images from 2018,2019,and 2020 were used to examine the broad applicability of the classification methodology.Two indices,a red/blue ratio and blue normalized difference vegetation index(blue NDVI),were effective at differentiating tawny and gray pubescence types in high-resolution imagery.A ML approach using a support vector machine(SVM)radial basis function(RBF)classifier was able to differentiate the gray and tawny types(83.1%accuracy and kappa=0:740 on a pixel basis)on images where reference training data was present.The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel,indicating limitations of using aerial imagery for pubescence classification in some environmental conditions.High-throughput classification of gray and tawny pubescence types is possible using aerial imagery,but light tawny soybeans remain difficult to classify and may require training data from each field season.

关 键 词：indices SPECTRAL consuming 

分 类 号：S565.1[农业科学—作物学]