Data integration of humidity sensor and image texture for water content prediction of Gracilaria sp. during sun drying

Abstract

Water content on site-measurement of dried seaweed required method with a minimum time of sample preparation time, less destructive effect to the sample, and could be validated. This research aimed to evaluate the potency of some features consist of image texture, resistance, and capacitance data of humidity sensor to predict water content changing of seaweed Gracilaria sp. during sun-drying. Dried Gracilaria sp. was rehydrated before being used in sun-drying for 4 hours. Gravimetrically-based water content evaluation, digital image taking, and measurement of resistance and capacitance value were conducted every 30 minutes interval during the drying. Images captured and collected by webcam in a conditioned lighting chamber were used subsequently for extraction of image texture features while a humidity sensor array contained 2 resistive sensors and 1 capacitive sensor respectively were applied to collect resistance and capacitance data. Collected data were used to create 4 datasets i.e. (1) 54 image texture features; (2) 3 resistance and capacitance features; (3) 57 features combination of dataset 1 and 2; and (4) 11 Features selected from dataset 3. Correlation coefficient and Root Mean Square Error of 4 datasets were applied for model evaluation utilized Multiple Linear Regression (MLR) and Multiple Layer Perceptron-based Neural Network (MLPNN). Investigation with cross-validation 10 folds test showed that MLPNN was the best model applied for dataset 1 with correlation coefficient and RMSE reached 0.89 and 9.11 respectively. Data integration of humidity sensor and image texture showed substantial potency to be used for the prediction of water content during sun drying.