scholarly journals Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

2021 ◽  
Vol 105 ◽  
pp. 102617
Author(s):  
Sheng Wang ◽  
Kaiyu Guan ◽  
Zhihui Wang ◽  
Elizabeth A. Ainsworth ◽  
Ting Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiative Transfer ◽  
Hyperspectral Imaging ◽  
Nitrogen Deficiency ◽  
Radiative Transfer Modeling

scholarly journals Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3827
Author(s):  
Gemma Urbanos ◽  
Alberto Martín ◽  
Guillermo Vázquez ◽  
Marta Villanueva ◽  
Manuel Villa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessel ◽  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Venous Blood ◽  
Healthy Tissue ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

scholarly journals Microwave temperature and pressure measurements with the Odin satellite: I. Observational method

2002 ◽  
Vol 80 (4) ◽  
pp. 443-454 ◽  
Author(s):  
J R Pardo ◽  
M Ridal ◽  
D Murtagh ◽  
J Cernicharo
Keyword(s):  
Radiative Transfer ◽  
Outer Space ◽  
Atmospheric Temperature ◽  
Upper Atmosphere ◽  
Operational Mode ◽  
Altitude Range ◽  
Molecular Lines ◽  
Temperature And Pressure ◽  
Radiative Transfer Modeling ◽  
Temperature Vertical Profile

The Odin satellite is equipped with millimetre and sub-millimetre receivers for observations of several molecular lines in the middle and upper atmosphere of our planet (~25–100 km, the particular altitude range depending on the species) for studies in dynamics, chemistry, and energy transfer in these regions. The same receivers are also used to observe molecules in outer space, this being the astrophysical share of the project. Among the atmospheric lines that can be observed, we find two corresponding to molecular oxygen (118.75 GHz and 487.25 GHz). These lines can be used for retrievals of the atmospheric temperature vertical profile. In this paper, we describe the radiative-transfer modeling for O2 in the middle and upper atmosphere that we will use as a basis for the retrieval algorithms. Two different observation modes have been planned for Odin, the three-channel operational mode and a high-resolution mode. The first one will determine the temperature and pressure on an operational basis using the oxygen line at 118.75 GHz, while the latter can be used for measurements of both O2 lines, during a small fraction of the total available time for aeronomy, aimed at checking the particular details of the radiative transfer near O2 lines at very high altitudes (>70 km). The Odin temperature measurements are expected to cover the altitude range ~30–90 km. PACS Nos.: 07.57Mj, 94.10Dy, 95.75Rs

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