Drying kinetics and image-based identification of drying end point during parboiling of komal chawal

During the process of parboiling, steamed rice goes through a process of drying till a suitable moisture content for storage. This work is based on classifying suitable moisture content of less than 13% for storage. Further, this classification is used for estimating the end point of drying based on images of rice taken out at different time intervals. The drying process is a basic process for food technologists and involves mathematical modeling to determine the process parameters. Optimal drying time calculation to achieve desired moisture content involves the use of mathematical modeling. There are two ways of calculating the end point. One is using already existing drying models, e.g., Page, Newton, etc., with time as a factor; the other is using FF- ANN with one hidden layer with drying time and temperature as a factor; and for theoretical understating of the diffusion process, the analytical solution of Fick's law was studied. The other method involves using extrinsic data like the RGB values and pixel values of images mapped with moisture content classes predicted using machine learning models. This study aims to show how RGB data-based machine learning models for classification gives real time solutions to problems like calculating the final moisture content after drying of parboiled brown rice. Digital images were used as input, pixel values were used as features, and the images were labeled into two categories: A (moisture greater than 13%) and B (less than or equal to 13%). For this purpose, the dataset used consists of the moisture content of samples at different times (0–180 min) and temperatures (40, 50, and 60 °C) of drying in a tray dryer. Semi empirical models were evaluated, and Modified-Page showed the best fitting, The Feed forward Artificial Neural Network was observed to show an accuracy of 0.882 based on the least square error. A convolutional neural network (CNN) was used to map the image pixel data to classify the rice sample based on moisture content with a validation accuracy of 0.79 and a test accuracy of 0.74. Similarly, RGB values-based ML models like CART showed better accuracy of 0.92 approximately. Lastly, a simple calculator for an android application was built in MIT App Inventor by selecting the best fitted model of classification (LR, KNN, NB, or SVM) based on RGB values as the features.