Data for: Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf

Name: Data for: Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf
Creator: Xinjie Yu
Published: 2017-12-29T16:45:48.906Z
Keywords: Regression Model

Yu, Xinjie; Lu, Huanda; Liu, Qiyu

doi:10.17632/syxf4m86gk.1

Data for: Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf

Published: 29 December 2017| Version 1 | DOI: 10.17632/syxf4m86gk.1

Contributors:

Xinjie Yu, Huanda Lu, Qiyu Liu

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(1)SAE-FNNtrain.py #Pre-train SAE model and fine-tune SAE-FNN model; (2)SAE-FNNpredict.py #Use the trained SAE-FNN regression model to get calibration and prediction results of the N concentration; (3)data\Meanspectra.csv #192 mean spectra of leaf samples in calibration and prediction set; (4)data\train_pixels\ #102528 pixel spectra obtained from ROI images.

Data for: Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf

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