Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination

Muhammad Zainal Altim, Abdullah Basalamah, kasman kasman

Abstract


The purpose of this study is to design and build a CNN deep learning program modeling for a mobile application for rice quality classification and analyze the performance of a mobile application-based classification program as a means of halal information in real time. The method applied is an experimental method that utilizes machine learning technology by using many rice images that are used as datasets. The data of these images is classified by their shape, color and background. This image is used as a reference for the training dataset. After the CNN training model is formed, it is then set up in a web editor p5.js then an interface is created to connect to a server such as Google Cloud using FastAPI, which can be accessed using a mobile application or a web server such as Chrome. In the mobile application, create an interface to connect with the camera system and data base on the cloud server. The results of the study were obtained that CNN deep learning modeling can be used in real time. In web browser usage, the data shown is also affected by lighting. The accuracy level of the built model reached above 99.8 percent with a validation accuracy rate of 99.7 percent in the data training process. When testing, the average accuracy of the data was around 99.9 percent. This clearly proves that CNNs can be used to classify objects properly and accurately.

Keywords


CNN, rice, data set, training, mobile application, web browser, real time.CNN, rice, data set, training, mobile application, web browser, real time.

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References


H. P. Aldino and R. Septiano, “Pengaruh Penggunaan Sistem Informasi Akuntansi, Teknologi Informasi, Pengendalian Internal Dan Kualitas Sumber Daya Manusia Terhadap Kualitas Laporan Keuangan,” J. Menara Ekon. Penelit. Dan Kaji. Ilm. Bid. Ekon., 2021, doi: 10.31869/me.v7i2.2865.

M. Mayadi and A. Anggrawan, “Pengembangan Sistem Informasi Pemantauan Harga Beras Dan Gabah Dengan Short Message Gateway,” Matrik J. Manaj. Tek. Inform. Dan Rekayasa Komput., 2022, doi: 10.30812/matrik.v21i2.1546.

B. Utomo, “Peran Penting Lembaga Pangan Dan Generasi Milenial Di Era Industri 4.0 Dalam Mendukung Ketahanan Pangan Nasional,” J. Pangan, 2020, doi: 10.33964/jp.v29i1.479.

V. Tasril, K. Khairul, and F. Wibowo, “Aplikasi Sistem Informasi Untuk Menentukan Kualitas Beras Berbasis Android Pada Kelompok Tani Jaya Makmur Desa Benyumas,” J. Inform., 2019, doi: 10.36987/informatika.v7i3.1384.

Z. Zulkifli, T. Nurhayatie, and M. Junaidi, “Penelitian Konseptual: Peningkatan Kualitas Strategis Melalui Penggunaan Teknologi Informasi Dan Komunikasi Dalam Meningkatkan Kinerja Pemerintah Daerah,” J. Litbang Sukowati Media Penelit. Dan Pengemb., 2019, doi: 10.32630/sukowati.v3i1.113.

L. Sumaryanti, “Analisis Citra Digital Untuk Klasifikasi Kualitas Beras,” Mustek Anim Ha, 2018, doi: 10.35724/mustek.v7i2.908.

N. Rahmadhani, “Pengumpulan Data Produktivitas Tanaman Pangan Pada Masa Pandemi Covid-19,” Semin. Nas. Off. Stat., 2021, doi: 10.34123/semnasoffstat.v2020i1.440.

A A Je Veggy Priyangka and I. M. S. Kumara, “Classification of Rice Plant Diseases Using the Convolutional Neural Network Method,” Lontar Komput. J. Ilm. Teknol. Inf., 2021, doi: 10.24843/lkjiti.2021.v12.i02.p06.

Md. S. I. Prottasha and S. M. S. Reza, “A Classification Model Based on Depthwise Separable Convolutional Neural Network to Identify Rice Plant Diseases,” Int. J. Electr. Comput. Eng. Ijece, 2022, doi: 10.11591/ijece.v12i4.pp3642-3654.

J. Liu, M. Xu, X. Xu, and Y. Huang, “Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy,” Entropy, 2019, doi: 10.3390/e21111070.

S. Cheng, K. Zhao, Y. Wang, and S. Cheng, “Water Quality Anomaly Monitoring Based on Kalman Filter and Convolution Neural Network,” Destech Trans. Comput. Sci. Eng., 2019, doi: 10.12783/dtcse/icaic2019/29433.

M. A. Taye, D. Morrow, J. D. Cull, D. H. Smith, and M. T. Hagan, “Deep Learning for FAST Quality Assessment,” J. Ultrasound Med., 2022, doi: 10.1002/jum.16045.

D. Selvaraj and A. Venkatesan, “Design of Accurate Multi-Class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification,” 2023, doi: 10.21203/rs.3.rs-2906389/v1.

M. Byra et al., “Transfer Learning With Deep Convolutional Neural Network for Liver Steatosis Assessment in Ultrasound Images,” Int. J. Comput. Assist. Radiol. Surg., 2018, doi: 10.1007/s11548-018-1843-2.

H. Devan, D. Farmery, L. Peebles, and R. Grainger, “Evaluation of Self-Management Support Functions in Apps for People With Persistent Pain: Systematic Review,” Jmir Mhealth Uhealth, 2019, doi: 10.2196/13080.

H. Zia, H. S. Fatima, M. Khurram, I. U. Hassan, and M. Ghazal, “Rapid Testing System for Rice Quality Control Through Comprehensive Feature and Kernel-Type Detection,” Foods, 2022, doi: 10.3390/foods11182723.

B. Jin et al., “Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined With Deep Learning,” Acs Omega, 2022, doi: 10.1021/acsomega.1c04102.

J. J. Walsh, A. Neupane, A. Koirala, M. M. Li, and N. Anderson, “Review: The Evolution of Chemometrics Coupled With Near Infrared Spectroscopy for Fruit Quality Evaluation. II. The Rise of Convolutional Neural Networks,” J. Infrared Spectrosc., 2023, doi: 10.1177/09670335231173140.

J. Zhang et al., “Rice Bacterial Blight Resistant Cultivar Selection Based on Visible/Near-Infrared Spectrum and Deep Learning,” Plant Methods, 2022, doi: 10.1186/s13007-022-00882-2.

F. Liu, B. Wu, Y. He, and C. Zhang, “Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection,” Front. Plant Sci., 2021, doi: 10.3389/fpls.2021.693521.

M. Fatima et al., “Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification,” Comput. Intell. Neurosci., 2022, doi: 10.1155/2022/1339469.

M. Köklü, İ. Çinar, and Y. S. Taşpınar, “Classification of Rice Varieties With Deep Learning Methods,” Comput. Electron. Agric., 2021, doi: 10.1016/j.compag.2021.106285.

M. Z. Altim, A. Basalamah, R. A. Syamsul, and A. Yudhistira, “Implementasi Convolutional Neural Network (CNN) Untuk Penentuan Kualitas Beras Berdasarkan Bentuk dan Warna,” vol. 8, 2023.

J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-Excitation Networks,” Ieee Trans. Pattern Anal. Mach. Intell., 2020, doi: 10.1109/tpami.2019.2913372.

P. Tejaswini, P. Singh, M. Ramchandani, Y. Rathore, and R. R. Janghel, “Rice Leaf Disease Classification Using CNN,” Iop Conf. Ser. Earth Environ. Sci., 2022, doi: 10.1088/1755-1315/1032/1/012017.

P. A. S. Rani and N. S. Singh, “Paddy Leaf Symptom-Based Disease Classification Using Deep CNN With ResNet-50,” Int. J. Adv. Sci. Comput. Eng., 2022, doi: 10.30630/ijasce.4.2.83.

M. Khoiruddin, A. Junaidi, and W. A. Saputra, “Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network,” J. Dinda Data Sci. Inf. Technol. Data Anal., 2022, doi: 10.20895/dinda.v2i1.341.

A. Sendjasni, M.-C. Larabi, and F. A. Cheikh, “Convolutional Neural Networks for Omnidirectional Image Quality Assessment: Pre-Trained or Re-Trained?,” 2021, doi: 10.1109/icip42928.2021.9506192.

R. Bello-Cerezo, F. Bianconi, F. D. Maria, P. Napoletano, and F. Smeraldi, “Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification Under Ideal and Realistic Conditions,” Appl. Sci., 2019, doi: 10.3390/app9040738.

P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Rice False Smut Detection Based on Faster R-CNN,” Indones. J. Electr. Eng. Comput. Sci., 2020, doi: 10.11591/ijeecs.v19.i3.pp1590-1595.

Y. Xiang et al., “PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile Devices,” 2022, doi: 10.48550/arxiv.2206.08662.

S. Dey, S. K. Saha, A. K. Singh, and K. McDonald-Maier, “FruitVegCNN: Power- And Memory-Efficient Classification of Fruits &Amp; Vegetables Using CNN in Mobile MPSoC,” 2020, doi: 10.36227/techrxiv.12686051.v1.

G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion,” Ieee Access, 2019, doi: 10.1109/access.2019.2943454.

D. Choe, E. Choi, and D. K. Kim, “The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning,” Mob. Inf. Syst., 2020, doi: 10.1155/2020/1475164.

Y. Liu et al., “Recent Advances in Plant Disease Severity Assessment Using Convolutional Neural Networks,” 2022, doi: 10.21203/rs.3.rs-1904357/v1.

T. Ahmed, C. R. Rahman, and Md. F. M. Abid, “Rice Grain Disease Identification Using Dual Phase Convolutional Neural Network Based System Aimed at Small Dataset.,” Agrirxiv, 2021, doi: 10.31220/agrirxiv.2021.00062.

A. Thammastitkul and J. Petsuwan, “Thai Hom Mali Rice Grading Using Machine Learning and Deep Learning Approaches,” Iaes Int. J. Artif. Intell. Ij-Ai, 2023, doi: 10.11591/ijai.v12.i2.pp815-822.

R. Deng et al., “Automatic Diagnosis of Rice Diseases Using Deep Learning,” Front. Plant Sci., 2021, doi: 10.3389/fpls.2021.701038.

A. F. L. Almeida, N. P. d. Rocha, and A. Silva, “Methodological Quality of Manuscripts Reporting on the Usability of Mobile Applications for Pain Assessment and Management: A Systematic Review,” Int. J. Environ. Res. Public. Health, 2020, doi: 10.3390/ijerph17030785.

R. Alfred, J. H. Obit, C. P. Chin, H. Haviluddin, and Y. Lim, “Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks,” Ieee Access, 2021, doi: 10.1109/access.2021.3069449.

A. Silva, P. Simões, R. Santos, A. Queirós, N. P. d. Rocha, and M. Rodrigues, “A Scale to Assess the Methodological Quality of Studies Assessing Usability of Electronic Health Products and Services: Delphi Study Followed by Validity and Reliability Testing,” J. Med. Internet Res., 2019, doi: 10.2196/14829.

H. Jo et al., “Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea,” Ieee Trans. Geosci. Remote Sens., 2020, doi: 10.1109/tgrs.2020.2981671.

P. Agarwal et al., “Assessing the Quality of Mobile Applications in Chronic Disease Management: A Scoping Review,” NPJ Digit. Med., 2021, doi: 10.1038/s41746-021-00410-x.

P. Hemalatha, G. Shankar, and D. M. D. Raj, “A New Improved Binary Convolutional Model for Classification of Images,” Scalable Comput. Pract. Exp., vol. 23, no. 4, pp. 263–274, 2022, doi: 10.12694/scpe.v23i4.2029.

Y. Zaky, N. Fortino, B. Miramond, and J.-Y. Dauvignac, “Shape and Orientation Classification of Objects Based on Their Electromagnetic Signatures Using Convolutional Neural Networks,” Inverse Probl., vol. 40, no. 4, p. 045027, 2024, doi: 10.1088/1361-6420/ad2ec9.

R. J. Moreno and P. C. Useche-Murillo, “Classification and Grip of Occluded Objects,” Indones. J. Electr. Eng. Inform. Ijeei, vol. 9, no. 1, 2021, doi: 10.52549/ijeei.v9i1.1846.

H. Oumarou, Y. Siradj, R. Rizal, and F. Candra, “Stabilization of Image Classification Accuracy in Hybrid Quantum-Classical Convolutional Neural Network With Ensemble Learning,” Innov. Res. Inform. Innov., vol. 6, no. 1, 2024, doi: 10.37058/innovatics.v6i1.10437.

I. A. Anjani, Y. R. Pratiwi, and S. N. B. Nurhuda, “Implementation of Deep Learning Using Convolutional Neural Network Algorithm for Classification Rose Flower,” J. Phys. Conf. Ser., vol. 1842, no. 1, p. 012002, 2021, doi: 10.1088/1742-6596/1842/1/012002.




DOI: https://doi.org/10.33387/protk.v12i1.9396

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