Bemisia tabaci (sensu latu) is a group of >40 highly cryptic whitefly species that are of global agricultural importance, both as crop pests and plant-virus vectors. Two devastating cassava diseases in East and Central Africa are spread by abundant populations of one of these species termed Sub-Saharan Africa 1 (SSA1). There is a substantive risk that these whitefly-borne pandemics will continue to spread westwards and disrupt cassava production for millions of smallholder farmers in West Africa. We report here, therefore, the first comprehensive survey of cassava B. tabaci in eastern Nigeria, a West African region likely to be the first affected by the arrival of these whitefly-borne pandemics. We found one haplotype comprising 32 individuals with 100% identical mtCO1 sequence to the East African SSA1 populations (previously termed SSA1-SG1) and 19 mtCO1 haplotypes of Sub-Saharan Africa 3 (SSA3), the latter being the most prevalent and widely distributed B. tabaci species in eastern Nigeria. A more divergent SSA1 mtCO1 sequence (previously termed SSA1-SG5) was also identified in the region, as were mtCO1 sequences identifying the presence of the MED ASL B. tabaci species and Bemisia afer. Although B. tabaci SSA1 was found in eastern Nigeria, they were not present in the high abundances associated with the cassava mosaic (CMD) and cassava brown streak disease (CBSD) pandemics of East and Central Africa. Also, no severe CMD or any CBSD symptoms were found in the region.

BackgroundThe paper introduces a multispectral imaging system and data-processing approach for the identification and discrimination of morphologically indistinguishable cryptic species of the destructive crop pest, the whitefly Bemisia tabaci. This investigation and the corresponding system design, was undertaken in two phases under controlled laboratory conditions. The first exploited a prototype benchtop variant of the proposed sensor system to analyse four cryptic species of whitefly reared under similar conditions. The second phase, of the methodology development, employed a commercial high-precision laboratory hyperspectral imager to recover reference data from five cryptic species of whitefly, immobilized through flash freezing, and taken from across four feeding environments.ResultsThe initial results, for the single feeding environment, showed that a correct species classification could be achieved in 85–95% of cases, utilising linear Partial Least Squares approaches. The robustness of the classification approach was then extended both in terms of the automated spatial extraction of the most pertinent insect body parts, to assist with the spectral classification model, as well as the incorporation of a non-linear Support Vector Classifier to maintain the overall classification accuracy at 88–98%, irrespective of the feeding and crop environment.ConclusionThis study demonstrates that through an integration of both the spatial data, associated with the multispectral images being used to separate different regions of the insect, and subsequent spectral analysis of those sub-regions, that B. tabaci viral vectors can be differentiated from other cryptic species, that appear morphologically indistinguishable to a human observer, with an accuracy of up to 98%. The implications for the engineering design for an in-field, handheld, sensor system is discussed with respect to the learning gained from this initial stage of the methodology development.

-The paper introduces a multispectral imaging system and data-processing approach for speciation of the insect viral vectors (whitefly) responsible for the transmission of Mosaic and Brown Streak viruses in Sub-Saharan African cassava. The trial results for four species of whitefly, on two plant leaf types (cassava & aubergine), are reported as well as the methodology applied. This indicates that a correct classification could be achieved in 85-95% of cases. The engineering design for an infield, handheld, variant of the technology is discussed alongside the learning gained from this initial study.