Cami Mosley, Christina Bennett Farrell, Charles T Quinn, Lisa Marie Shook
A Mixed-Methods Evaluation of a Project ECHO Program for the Evidence-Based Management of Sickle Cell Disease
International journal of environmental research and public health
identification sweet corn seed vitality essential criterion selecting highquality varieties research combination hyperspectral imaging technique diverse deep learning algorithms utilized identify different vitality grades sweet corn seeds first hyperspectral data 496 seeds including four viabilitygrade seeds extracted preprocessed support vector machine svm extreme learning machine elm used construct classification models finally onedimensional convolutional neural networks 1dcnn onedimensional long shortterm memory 1dlstm cnn combined lstm cnnlstm proposed firefly algorithm fa optimized cnnlstm facnnlstm utilized distinguish spectral images sweet corn seeds viability grade findings experimental analysis indicate deep learning models exhibit significant advantage traditional machine learning approaches discrimination seed vitality levels boasting classification accuracy exceeding 9426 test datasets achieving accuracy improvement least 3 compared bestperforming machine learning model moreover performance facnnlstm model proposed study demonstrated slight superiority three models besides facnnlstm achieved classification accuracy 9723 representing significant improvement 297 compared lowestperforming cnn 149 enhancement cnnlstm summary study reveals potential integrating deep learning hyperspectral imaging promising alternative discriminating sweet corn seed vitality grade showcasing value agricultural research cultivar breeding