Data for: Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging

Published: 18-03-2018| Version 1 | DOI: 10.17632/ffkv2xj2m7.1
Contributors:
Xinjie Yu,
Huanda Lu,
Di Wu

Description

(1)SAE-FNNtrain.PY: Python code of SAE-FNN model; (2)SAE-FNNpredict.PY: use the trained model to predict firmness and SSC; (3)data/PearMeanspectra.csv: 180 mean spectra and the corresponding firmness and SSC; (4)data/train_pixels/ramdonpixel_train.pkl.gz, data/train_pixels/ramdonpixel_val.pkl.gz: random pixel spectra for training the SAE-FNN model; (5)logs/*:trained model files for firmness and SSC prediction.

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