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

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.