Kathlyn Sharmaine Valdez, Paul Froilan Garma, Andrew Sumpay, Mickaela Gamboa, Ma Stefanie Reyes, Ma Carmela Gatchalian, Erwin Mendoza, Anna Alexis Forteza
Development and Preliminary Evaluation of Patient Perceptions on Safety Culture in a Hospital Setting Scale
Acta medica Philippina
estimating monitoring chlorophyll content critical step crop spectral image analysis quick nondestructive assessment chlorophyll content rice leaves optimize nitrogen fertilization benefit environment economy improve rice production management quality research spectral analysis rice leaves performed using hyperspectral fluorescence spectroscopy detection chlorophyll content rice leaves study generated ninety experimental spectral datasets collecting rice leaf samples farm sichuan province china implementing feature extraction algorithm study compresses redundant spectral bands subsequently constructs machine learning models reveal latent correlations among extracted features prediction capabilities six feature extraction methods four machine learning algorithms two types spectral data examined accurate method predicting chlorophyll concentration rice leaves devised ivsoivissa iteratively variable subset optimizationinterval variable iterative space shrinkage approach quadratic feature combination approach based fluorescence spectrum data best prediction performance among cnnlstm convolutional neural network long shortterm memory algorithms corresponding rmsetrain root mean squared error rmsetest rpd ratio standard deviation validation set standard error prediction indexes 026 029 264 respectively demonstrated study hyperspectral fluorescence spectroscopy analyzed feature extraction machine learning methods provide new avenue rapid nondestructive crop health monitoring critical advancement smart precision agriculture