Abstract-The popularity of using hyperspectral imaging systems in studying and monitoring plant properties and conditions has increased lately. This increase has been driven by both financial and environmental advantages of such systems. Using a nondestructive hyperspectral imaging system improves the breeding process, increases profit, and reduces the usage of herbicide, thus reducing side effects to plants and environment. This paper is concerned with the use of hyperspectral image analysis for differentiating different plant species as well as their conditions. The main contribution of the work lies in the use of feature selection for choosing relevant, discriminant spectral information as the input to the classifier (e.g. SVM), as compared to the use of empirical spectral indices. Two independent hyperspectral datasets, captured by different instrumentations, were used in evaluation. Experimental results show significant improvements in classification accuracy with several feature selection algorithms compared to with the spectral vegetation and disease indices. The study shows that systematically selection of wavelength features can shed light on attributes that differentiate plants and their conditions.
BackgroundThe use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance.ResultsThe proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation.ConclusionsThe ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.
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.
Abstract-A multispectral imaging system is presented, using components that will support its deployment within the world of small-holder agriculture. An active narrowband illumination setup was selected, which allowed a low-cost broadband image sensor to be used. The preliminary set-up has been demonstrated with droughted tomato plants as a proof of concept. The results demonstrated a 5, 28 and 90% deterioration after day 1, 2 and 3 respectively; calculated by the disease/water stress index. Initial analysis showed that for specific applications the device be used in lieu of high-cost diffraction gratings, however additional innovation is required to negate unwanted sensing phenomena.
-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.
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