icipe Digital Repository

Is it possible to discern striga weed (Striga hermonthica) infestation levels in maize agroecological systems using in-situ spectroscopy?

Show simple item record

dc.contributor.author Mudereri, B. T.
dc.contributor.author Dube, T.
dc.contributor.author Niassy, S.
dc.contributor.author Kimathi, E.
dc.contributor.author Landmann, T.
dc.contributor.author Khan, Z. R.
dc.contributor.author Abdel-Rahman, E.
dc.date.accessioned 2020-03-13T07:17:06Z
dc.date.available 2020-03-13T07:17:06Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/123456789/1188
dc.description Research Paper en_US
dc.description.abstract The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and hascaused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminateplant species, providing possibilities to track such weed invasions and improve precision agriculture. However,essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. Inthis study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops andweeds. We used thein-situFieldSpec®Handheld 2™ analytical spectral device (ASD), hyperspectral data and theirrespective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum(EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis:LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga(Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 wa-veband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. Thein-situhyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 usingpublished spectral response functions. We sampled and detected seven Striga infestation classes based on threeflowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture ofmaize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to selectthe most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algo-rithms was compared using classification accuracy assessment metrics. We were able to positively discriminateStriga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurringweeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampledSentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highestoverall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands,respectively, across the four different discriminant models tested in this study. The class with the highest de-tection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurringweeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 mostrelevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectralwavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectralwavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hy-perspectral features (i.e. wavebands and VIs) significantly (p≤ 0.05) increased the overall classification accu-racy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Ourresults show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learningdiscriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems en_US
dc.description.sponsorship Biovision Foundation forEcological Development (Switzerland) and grant number (BV DPP-010/2019); UK’s Department for International Development (DFID);Swedish International Development Cooperation Agency (Sida); theSwiss Agency for Development and Cooperation (SDC); FederalDemocratic Republic of Ethiopia; and the Kenyan Government. “BTM”was supported by a German Academic Exchange Service (DAAD) In-Region Postgraduate Scholarship en_US
dc.publisher Elsevier en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Invasive weeds detection en_US
dc.subject Maize en_US
dc.subject In-situhyperspectral data en_US
dc.subject Machine learning en_US
dc.subject Resampled Sentinel-2 en_US
dc.title Is it possible to discern striga weed (Striga hermonthica) infestation levels in maize agroecological systems using in-situ spectroscopy? en_US
dc.type Article en_US


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States

Search icipe Repository


Advanced Search

Browse

My Account