Varietal discrimination of common dry bean (Phaseolus vulgaris L.) grown under different watering regimes using multitemporal hyperspectral data

Abstract. Globally, the common dry bean varieties (Phaseolus vulgaris L.) are regarded as valuable food crops. Due to diversion-farm and postharvest management requirements, quick, reliable, and cost-effective varietal discrimination is critical for optimal management during growth and after harvesting. The large number of valuable wavelengths that characterize hyperspectral remotely sensed datasets in concert with emerging robust discriminant analysis techniques offers great potential for on-farm dry bean varietal discrimination. In this study, an integrated approach of partial least-squares discriminant analysis (PLS-DA) on hyperspectral data was used to determine the bean’s optimal timing for on-farm varietal discrimination. Based on experimental plots underirrigated and rain-fed watering regimes, hyperspectral data were collected at three major phenological stages. Data at each stage were first used to generate PLS-DA models to determine variable (wavebands) importance in the projection (VIP) and the VIP bands used to generate VIP conditioned PLS-DA models. The study identified 6 weeks (branching and rapid vegetative growth) and 10 weeks (flowering and pod development) after seed sowing as optimal stages for varietal discrimination. The study offers insight into the optimal period to discriminate dry bean varieties using spectroscopy, valuable for on-farm and after-farm management and crop monitoring sensor development.

[1]  O. Mutanga,et al.  Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry , 2009 .

[2]  José Alexandre Melo Demattê,et al.  Discrimination of sugarcane varieties using Landsat 7 ETM+ spectral data , 2006 .

[3]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[4]  Andrew K. Skidmore,et al.  Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Paul J. Curran,et al.  Imaging Spectrqmetry - Its Present And Future RÔLe In Environmental Research , 1994 .

[6]  A. Skidmore,et al.  Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features , 2004 .

[7]  Daniel G. Debouck,et al.  Morphology of the common bean plant Phaseolus vulgaris , 1986 .

[8]  Onisimo Mutanga,et al.  Investigating the Capability of Few Strategically Placed Worldview-2 Multispectral Bands to Discriminate Forest Species in KwaZulu-Natal, South Africa , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  M. Govender,et al.  A review of hyperspectral remote sensing and its application in vegetation and water resource studies , 2009 .

[10]  Clayton C. Kingdon,et al.  Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario , 2008 .

[11]  A. Skidmore,et al.  Exploring spectral discrimination of grass species in African rangelands , 2001 .

[12]  Charles S. Wortmann,et al.  Atlas of common bean (Phaseolus vulgaris L.) production in Africa , 1998 .

[13]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[14]  A. Formaggio,et al.  Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data , 2005 .

[15]  John M. Kovacs,et al.  Separating Crop Species in Northeastern Ontario Using Hyperspectral Data , 2014, Remote. Sens..

[16]  M. H. Kalubarme.  Geo-informatics and Remote Sensing Applications for Village-level Crop Inventory in Gujarat State, India , 2013 .

[17]  G. Sridhar,et al.  Use of remote sensing for drought stress monitoring, yield prediction and varietal evaluation in castor beans (Ricinus communis L.) , 2005 .

[18]  N. L. Afanador,et al.  Important variable selection in partial least squares for industrial process understanding and control , 2014 .

[19]  R. Pu,et al.  Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features , 2001 .

[20]  Yubin Lan,et al.  Differentiation of Cotton from Other Crops at Different Growth Stages Using Spectral Properties and Discriminant Analysis , 2012 .

[21]  M. Tenenhaus,et al.  Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach , 2003, Human Genetics.

[22]  Chen Zhongxin,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008 .

[23]  Jean-Michel Roger,et al.  Discrimination of Corn from Monocotyledonous Weeds with Ultraviolet (UV) Induced Fluorescence , 2011, Applied spectroscopy.

[24]  A. Thomsen,et al.  Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression , 2002, The Journal of Agricultural Science.

[25]  Armando Apan,et al.  Spectral discrimination and classification of sugarcane varieties using EO-1 hyperion hyperspectral imagery , 2004 .

[26]  Pangirayi Tongoona,et al.  Genetic and GGE biplot analyses of sorghum germplasm for stem sugar traits in Southern Africa , 2012 .

[27]  Jeffrey W. White,et al.  Growth habit and gene pool effects on inheritance of yield in common bean , 2004, Euphytica.

[28]  I. Sobhan,et al.  Species discrimination from a hyperspectral perspective , 2007 .

[29]  Onisimo Mutanga,et al.  Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa , 2013 .

[30]  Reyer Zwiggelaar,et al.  A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops , 1998 .

[31]  Chaichoke Vaiphasa,et al.  Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data , 2013, Remote. Sens..