Vinay Saggar, Philip O'Donnell, Hillary Moss, Andrew Yoon, Carlo Lutz, Andrew Restivo, Oark Ahmed, Debayan Guha, Farrukh Jafri, Maninder Singh
Effectiveness of a virtual reality trainer for retention of tourniquet application skills for hemorrhage control among emergency medicine residents
AEM education and training
mixing cotton seeds different cultivars qualities lead differences growth conditions make field management difficult particular except yield loss also lead inconsistent cotton quality poor textile product quality causing huge economic losses farmers cotton processing industry however traditional cultivar identification methods cotton seeds timeconsuming laborintensive cumbersome cannot meet needs modern agriculture modern cotton processing industry therefore urgent need fast accurate nondestructive method identifying cotton seed cultivars study hyperspectral images 39732 nm100358 nm five cotton cultivars namely jinke 20 jinke 21 xinluzao 64 xinluzao 74 zhongmiansuo 5 captured using specim iq camera average spectral information seeds cultivar used spectral analysis aiming establish cotton seed cultivar identification model due presence many obvious noises 400 nm 1000 nm regions collected spectral data spectra 400 nm 1000 nm selected representative spectra seed samples various denoising techniques including savitzkygolay sg standard normal variate snv first derivative fd applied individually combination improve quality spectra additionally successive projections algorithm spa employed spectral feature selection based fullband spectra partial least squaresdiscriminant analysis plsda model established furthermore spectral features textural features fused create random forest rf convolutional neural network cnn extreme learning machine elm identification models results showed 1 snvfd preprocessing method showed optimal denoising performance 2 spa highlighted nearinfrared region 8001000 nm red region 620700 nm bluegreen region 420570 nm identifying cotton cultivar 3 fusion spectral features textural features consistently improve accuracy modeling strategies suggesting need research appropriate modeling strategies 4 elm model highest cotton cultivar identification accuracy accuracy 100 training set 9889 test set conclusion study successfully developed highly accurate cotton seed cultivar identification model elm model study provides new method rapid nondestructive identification cotton seed cultivars help ensure cultivar consistency seeds used cotton planting improve overall quality yield cotton