Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

Febus Reidj G. Cruz; Dionis A. Padilla; Carlos C. Hortinela IV; Krissel C. Bucog; Mildred C. Sarto; Nirlu Sebastian A. Sia; Wen-Yaw Chung

doi:10.1117/12.2266947

8 February 2017 Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

Febus Reidj G. Cruz, Dionis A. Padilla, Carlos C. Hortinela IV, Krissel C. Bucog, Mildred C. Sarto, Nirlu Sebastian A. Sia, Wen-Yaw Chung

Author Affiliations +

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102251W (2017) https://doi.org/10.1117/12.2266947
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

This study is about the determination of moisture content of milled rice using image processing technique and perceptron neural network algorithm. The algorithm involves several inputs that produces an output which is the moisture content of the milled rice. Several types of milled rice are used in this study, namely: Jasmine, Kokuyu, 5-Star, Ifugao, Malagkit, and NFA rice. The captured images are processed using MATLAB R2013a software. There is a USB dongle connected to the router which provided internet connection for online web access. The GizDuino IOT-644 is used for handling the temperature and humidity sensor, and for sending and receiving of data from computer to the cloud storage. The result is compared to the actual moisture content range using a moisture tester for milled rice. Based on results, this study provided accurate data in determining the moisture content of the milled rice.

Citation Download Citation

Febus Reidj G. Cruz, Dionis A. Padilla, Carlos C. Hortinela IV, Krissel C. Bucog, Mildred C. Sarto, Nirlu Sebastian A. Sia, and Wen-Yaw Chung "Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251W (8 February 2017); https://doi.org/10.1117/12.2266947

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