scholarly journals Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1944 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Fucheng Lin ◽  
Zhenzhu Su ◽  
Kangpei Yuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Blue Mold ◽  
Artificial Neural

scholarly journals Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0209451 ◽  
Author(s):  
Masabho P. Milali ◽  
Maggy T. Sikulu-Lord ◽  
Samson S. Kiware ◽  
Floyd E. Dowell ◽  
George F. Corliss ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Artificial Neural ◽  
Age Grading

Applying Artificial Neural Networks: Part II. Using near Infrared Data to Classify Tobacco Types and Identify Native Grown Tobacco

1997 ◽  
Vol 5 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Maha Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M.W. White ◽  
D.R. Bahler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Near Infrared ◽  
Back Propagation ◽  
Calibration Model ◽  
Linear Network ◽  
Burley Tobacco ◽  
The Usa ◽  
Artificial Neural ◽  
Propagation Network

Classification of flue-cured and Burley tobacco types with artificial neural networks (ANNs) were studied. Burley tobacco was further classified as either grown in the USA or grown outside the USA. The input data were in the form of near infrared (NIR) spectra, each spectrum containing 19 points. The number of flue-cured and Burley samples were 654 and 959, respectively. The number of native and non-native tobacco samples were 266 and 267, respectively. The models selected for this research were a quadratic classifier, a back-propagation network and a linear network. The results of the calibration model and the true performance for classifying tobacco species were (100%, 100%), (99.38%, 99.39%) and (95.19%, 99.26%) for the quadratic classifier, back-propagation network and linear network, respectively. The identification of native tobacco and its true performance were (100%, 100%) using a quadratic classifier, (89.12%, 88.46%) using a back-propagation network and (80.68%, 79.62%) using a linear network.

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