Hierarchical classification pathway for white maize, defect and foreign material classification using spectral imaging

This study aimed to present the South African maize industry with an accurate and affordable automated analytical technique for white maize grading using near infrared (NIR) spectral imaging. The 17 categories and sub-categories stipulated in South African maize grading legislation were simultaneously classified (1044 samples; 60 kernels of each class) using 25 partial least squares discriminant analysis (PLS-DA) models. The models were assembled in a hierarchical decision pathway that progressed from the most easily classified classes to the most difficult. The full NIR spectrum (288 wavebands) model performed with an overall accuracy of 93.3% for the main categories. Three waveband selection techniques were employed, namely waveband windows (48 wavebands), variable importance in projection (VIP) (21 wavebands) and covariance selection (CovSel) (13 wavebands). Overall, the VIP set based on only 7.3% of the original spectral variables was recommended as the best trade-off between performance and expected cost of a reduced waveband system. © 2020 Elsevier B.V

Plant functional trait and hyperspectral reflectance responses to Comp B exposure: efficacy of plants as landmine detectors

At least 110 million landmines have been planted since the 1970s in about 70 nations, many of which remain in place today. Some risk of detection may be mitigated using currently available remote sensing techniques when vegetation is present. My study focused on using plants as phytosensors to detect buried explosives. I exposed three species representing different functional types (Cyperus esculentus (sedge), Ulmus alata (tree), Vitis labrusca (vine)) to 500 mg kg-1 of Composition B (Comp B; 60/40 mixture of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and 2,4,6-trinitrotoluene (TNT)), a commonly used explosive mixture, and measured functional traits and reflectance over a nine-week period. Cyperus esculentus was not a good indicator for the presence of explosive compounds. Comp B treatment woody species, U. alata and V. labrusca, exhibited changes in pigment content, leaf area, specific leaf area, dry leaf biomass, and canopy reflectance. The efficacy of plants as landmine detectors is species and/or functional group dependent

Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg-1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems

Hyperspectral Image Analysis for Mechanical and Chemical Properties of Concrete and Steel Surfaces

According to the 2017 ASCE Report Card, 39% and 15% of 614,387 bridges in the U.S. are more than 50 years and 40 to 49 years, respectively. The number of deficient bridges is increasing. One of the most common causes of reinforced concrete deterioration is corrosion of steel reinforcing bars. Currently, most bridges are visually inspected every two years using boom/snooper trucks to get access to various areas to be inspected. The subjective visual inspection often leads to inconsistent results that are less useful in bridge management. Hyperspectral camera, installed on an unmanned aerial vehicle, can potentially supplement visual inspection with quantifiable and reliable imagery from remote and safe operations. It can be used to identify physical characteristics (e.g., concrete cracks)and characterize chemical features (e.g., steel corrosion)

Educational Virtual Reality Visualisations of Heritage Sites.

This paper discusses the use of games engines to create virtual heritage applications. The use of 3D software for cultural or heritage applications is discussed with reference to the capabilities and potential of games engines. The contribution of students from Bournemouth University to the New Forest Heritage Mapping project through the creation of interactive virtual reality visualisations of historic landscapes is described. The creation and evaluation of three different applications representing three alternative interaction styles are discussed. The first does not indicate where information can be found, the second uses visible cues and the third implements an objective marker system