Identification of Inpari HDB 32 Superior Rice Seeds based on Android in Realtime with Artificial Neural Network

Authors

  • Rizalul Akram Department of Informatics, Universitas Samudra, Langsa , Indonesia
  • Teuku Hadi Wibowo Atmaja Department of Biology, Universitas Samudra, Langsa, Indonesia
  • Novianda Department of Informatics, Universitas Samudra, Langsa, Indonesia

DOI:

https://doi.org/10.15575/join.v9i2.941

Keywords:

Android, Classification, Neural Network, Realtime, Rice

Abstract

Rice is a staple food for humans living in East Asia. Rice is a crystal fruit. The Latin name for rice is Oryza Sativa. Rice plants are 110-120 days old. The selection of quality rice seeds by farmers is seen from the bright yellow color of the rice without black/brown spots, its large size and rounder. Rice seeds that are not of good quality are dark brown in color, have black/brown spots, and are flat in shape. The absence of superior rice recognition technology that is not Android-based in real time is the main reason for this research. The focus of this research is to identify superior and non-superior rice in Inpari HDB 32 rice with a high recognition accuracy rate of more than 70 percent with a viewing angle of 0-180 degrees using the real-time ANN method. The training data used in this research was 1000 datasets consisting of 350 superior rice datasets and 650 non-superior datasets. The smart model for classifying rice seeds that has been built in this research is generally able to run well where the classification accuracy obtained is quite good. The classification accuracy of the ANN model during training of the neural network model was able to classify rice seeds with an accuracy of 70-100% with the confidence value of the real-time classification results ranging from 65-98%. Real-time classification of rice grains with maximum accuracy of 96% and many grains 73%.

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2024-12-27

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