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Identification of Suitable Complex Machine Learning Algorithms for Amylose Content Prediction in Rice with an IoT-based Colorimetric Sensor

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Title Identification of Suitable Complex Machine Learning Algorithms for Amylose Content Prediction in Rice with an IoT-based Colorimetric Sensor
 
Creator Deshpande, Shrinivas
Nidoni, Udaykumar
Patil, Rahul
Hiregoudar, Sharanagouda
K T, Ramappa
Maski, Devanand
Naik, Nagaraj
 
Subject Ageing of rice
Amylose sensor
IoT device
Mathematical modeling
Rice quality
 
Description 102-113
Rice ageing is a complex phenomenon that is hard to investigate thoroughly. Many physicochemical qualities change
gradually because of moisture content and storage temperature. Among these characteristics, amylose quantity is particularly
essential, and most indexes rely on it. To address these challenges, various gadgets, IoT, ICT, AI and predictive technologies
are frequently applied in diagnostic procedures. This study evaluated AdaBoost, Artificial neural network (ANN), k-Nearest
Neighbour classifier (KNN), Decision tree, Logistic regression, Support Vector Machine (SVM), and Random forest
classifiers to categorize distinct quantities of amylose using slope data gathered from the novel colorimetric amylose sensor.
The random forest approach had greater coefficients and precision ratings of 0.85 for the slope dataset, followed by the
decision tree, ANN, KNN, AdaBoost, logistic regression, and support vector algorithms, which had precision scores of 0.83,
0.81, 0.80, 0.29, 0.18, and 0.18, respectively, based on the efficiency of the tested learning models. The random forest model
was shown to be promising in forecasting the various classes of amylose based on the data.
 
Date 2024-01-08T11:32:08Z
2024-01-08T11:32:08Z
2024-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63170
https://doi.org/10.56042/jsir.v83i1.2458
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(01) [January 2024]