Image-based Quality Identification of Black Soybean (Glycine soja) Using Convolutional Neural Network

Mas'ud Effendi, Naufal Hilmi Ramadhan, Arif Hidayat

Abstract


Abstract

The problem faced in identifying the quality of black soybeans is that the quality of the assessment is inconsistent and it takes a relatively long time. This study aims to determine the best convolutional neural network architecture by comparing the performance of Custom CNN, MobileNetV2, and ResNet-34 architectures in identifying the quality level (grade) of black soybeans. The quality of black soybean is split into 4 different classes based on physical characteristics (split, damaged, other colors, wrinkles, dirt) and moisture content test. The number of images used is 1300 images, with the ratio of training data, validation data, and testing data are 50:25:25, 60:25:15, and 70:20:10. The best model for identifying the quality based on the physical characteristics is the MobileNetV2 architecture with a ratio of 50:25:25 which produces an accuracy of 90.18%. Morover, the best model for identifying the quality based on the moisture content is the ResNet-34 architecture with a ratio of 70:20:10, which produces an accuracy of 78.12%. The best overall accuracy in identifying the quality based on both physical characteristics and moisture content is the ResNet-34 architecture, with a ratio of 70:20:10, with an average accuracy of testing data of 79.21%.

Keywords: black soybean, Convolutional Neural Network, image, MobileNetV2, ResNet-34

 

Abstrak

Permasalahan yang dihadapi dalam identifikasi mutu kedelai hitam adalah kualitas penilaian yang tidak konsisten dan membutuhkan waktu yang relatif lama. Penelitian ini bertujuan untuk menentukan arsitektur jaringan saraf konvolusional terbaik dengan membandingkan kinerja antara arsitektur Custom CNN, MobileNetV2, dan ResNet-34 dalam identifikasi tingkat mutu kedelai hitam. Mutu kedelai hitam terdiri dari 4 kelas dengan parameter uji fisik (belah, rusak, warna lain, keriput, kotoran) dan uji kadar air. Peneliti ini menggunakan1300 citra dengan rasio data latih, data validasi, dan data uji yang digunakan adalah 50:25:25, 60:25:15, dan 70:20:10. Hasil terbaik untuk identifikasi mutu parameter uji fisik adalah pada arsitektur MobileNetV2 dengan rasio 50:25:25 dan akurasi 90,18%. Hasil terbaik untuk identifikasi mutu parameter uji kadar air adalah arsitektur ResNet-34 dengan rasio 70:20:10 dan akurasi 78,12%. Hasil akurasi terbaik secara keseluruhan dengan identifikasi parameter fisik dan kadar air adalah arsitektur ResNet-34 dengan rasio 70:20:10 yang memiliki rata-rata akurasi data uji 79,21%.

Kata kunci: citra, Jaringan Saraf Konvolusional, kedelai hitam, MobileNetV2, ResNet-34

 


Keywords


black soybean; Convolutional Neural Network; image; MobileNetV2; ResNet-34; citra; Jaringan Saraf Konvolusional; kedelai hitam

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References


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https://doi.org/10.21776/ub.industria.2023.012.01.7

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