Implementation of Convolutional Neural Network in Detecting Avocado Ripeness Level
Abstract
Squeezing avocados to determine ripeness can cause physical damage or bruising, reducing the fruit’s quality and resulting in losses for sellers and buyers. This research aims to develop an Android-based mobile application to detect avocado ripeness based on skin color, avoiding physical damage to the fruit. The study uses three simple Convolutional Neural Network architectures to evaluate the algorithm’s ability to detect avocado ripeness. The dataset includes 385 images across four classes: immature, half-ripe, ripe, and overripe (74 images each), and an additional 89 images for the non-avocado class. The model was trained with learning rates of 0.001, 0.0001, and 0.00001. The architecture with the most convolutional layers achieved the best performance with a 0.001 learning rate, yielding a test accuracy of 94.15%, a test loss of 19.28%, and an F1-score of 94.0%. The best model was then converted to TFLite format and successfully integrated into an Android application that functions effectively.
Keywords
Full Text:
PDFReferences
K. Prihatman, “Alpukat ( Persea americana Mill / Persea gratissima Gaerth ),” 2000.
B. Schaffer, N. Wolsentholme, and A. Whiley, The avocado, vol. 16, no. 1. 2014. doi: 10.14321/fourthgenre.16.1.0075.
S. S. Harjadi, “AVOCADO PRODUCTION IN INDONESIA,” Bangkok, 2000.
C. A. Jaramillo-acevedo, W. E. Choque-valderrama, G. E. Guerrero-álvarez, and C. A. Meneses-escobar, “Hass avocado ripeness classi fi cation by mobile devices using digital image processing and ANN methods,” vol. 16, no. 12, 2020.
J. Saputra, Y. Sa, V. Yoga, P. Ardhana, and M. Afriansyah, “Klasifikasi Kematangan Buah Alpukat Mentega Menggunakan Metode K- Nearest Neighbor Berdasarkan Warna Kulit Buah,” vol. 3, no. 5, pp. 347–354, 2023.
I. Nuryani, A. M. N. Fadli, N. D. Saputri, and ..., “Classification of Avocado Ripeness Levels using Naïve Bayes Method,” Pros. Univ. …, pp. 468–473, 2022, [Online]. Available: http://repository.urecol.org/index.php/proceeding/article/view/2172
Mukhofifah and E. Nurraharjo, “SISTEM DETEKSI KEMATANGAN BUAH ALPUKAT MENGGUNAKAN METODE PENGOLAHAN CITRA,” vol. 11, no. 1, pp. 12–23, 2019.
E. R. Davies, Computer Vision: Principles, Algorithms, Applications, Learning: Fifth Edition. 2017.
M. W. Ilahi1, C. N. Apriyani, A. Desiani, N. Gofar, Y. Andriani, and M. R. Halim, “Classification of Geometric Batik Motif Typical of Indonesian Using Convolutional Neural Network,” vol. 15, no. 1, pp. 91–100, 2022.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
A. Arkadia, S. Ayu Damayanti, and D. Sandya Prasvita, “Klasifikasi Buah Mangga Badami Untuk Menentukan Tingkat Kematangan dengan Metode CNN,” Pros. Semin. Nas. Mhs. Bid. Ilmu Komput. dan Apl., vol. 2, no. 2, pp. 158–165, 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/1813
A. M. Lesmana, R. P. Fadhillah, and C. Rozikin, “Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN),” J. Sains dan Inform., vol. 8, no. 1, pp. 21–30, 2022, doi: 10.34128/jsi.v8i1.377.
M. A. Yusman, E. Evanita, and A. A. Riadi, “Klasifikasi Kematangan Buah Tin Menggunakan Convolutional Neural Network Berbasis Android,” J. Tek. Inform. dan Sist. Inf., vol. 9, no. 2, pp. 167–176, 2023, doi: 10.28932/jutisi.v9i2.5701.
B. P. Statistik, “Persentase Penduduk yang Memiliki/Menguasai Telepon Seluler Menurut Provinsi dan Klasifikasi Daerah 2019-2021.” [Online]. Available: https://www.bps.go.id/indicator/2/395/1/persentase-penduduk-yang-memiliki-menguasai-telepon-seluler-menurut-provinsi-dan-klasifikasi-daerah.html
N. Sari, E. Darmawati, and U. Ahmad, “Retail Packaging Design For Avocado Fruits Labeled Ripe Indicator,” J. Keteknikan Pertan., vol. 10, no. 3, pp. 291–304, 2022, doi: 10.19028/jtep.010.3.291-304.
B. H. Cho, K. Koyama, E. Olivares Díaz, and S. Koseki, “Determination of ‘Hass’ Avocado Ripeness During Storage Based on Smartphone Image and Machine Learning Model,” Food Bioprocess Technol., vol. 13, no. 9, pp. 1579–1587, 2020, doi: 10.1007/s11947-020-02494-x.
M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Computation, vol. 11, no. 3, 2023, doi: 10.3390/computation11030052.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
DOI: https://doi.org/10.31284/j.iptek.2025.v29i1.6737
Refbacks
- There are currently no refbacks.
Indexed by:
