Advanced Classification of Oil Palm Fruit Ripeness Using ResNet50 and Real-Time Image Analysis for Enhanced Agricultural Practices
DOI:
https://doi.org/10.56313/jictas.v3i2.395Keywords:
Oil Palm, Ripeness Classification, ResNet50, Convolutional Neural Network, Real-Time AnalysisAbstract
The classification of oil palm fruit ripeness is a critical factor in optimizing palm oil production. Traditional methods of ripeness assessment, based on the percentage of detached fruits and changes in skin color, are prone to human error due to subjective judgment. This study proposes an advanced approach utilizing deep learning with the ResNet50 model to classify oil palm fruit ripeness into four levels: unripe, under-ripe, ripe, and overripe. The research evaluates the model's performance under various data allocations, optimizers, and learning rates while incorporating data augmentation techniques to enhance accuracy. Experimental results indicate the optimal configuration includes a 90/10 data split, Adam optimizer, and a learning rate of 0.0001, achieving precision of 96%, recall of 98%, F1 score of 97%, and accuracy of 97%. These findings highlight the potential of ResNet50 in delivering reliable, real-time classification for agricultural applications, providing a practical solution for farmers and industries. The study concludes that large and diverse training datasets are essential for achieving robust classification results
References
L. Nurdianti, I. Wulandari, and F. Setiawan, “formulasi sediaan hand and body lotion ekstrak metanol buah paprika merah (Capsicum annuum Linnaeus) sebagai antioksidan,” Pros. Semin. Nas. Disem. Penelit., vol. 1, no. September, 2021.
B. Yanto, L. Fimawahib, A. Supriyanto, B. H. Hayadi, and R. R. Pratama, “Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna dengan Metode Deep Learning Convolutional Neural Network,” INOVTEK Polbeng - Seri Inform., vol. 6, no. 2, 2021, doi: 10.35314/isi.v6i2.2104.
B. Yanto, B. Basorudin, S. Anwar, A. Lubis, and K. Karmi, "Smart Home Monitoring System Using Face Identification with ESP32 Camera Based on IoT," Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 11, no. 1, pp. 53–59, 2022, doi: 10.24853/sisfokom.11.1.53-59.
Lubis, E. Prasiwinigrum, and S. Maulana, "Identification and Diagnosis Expert System Design for Oil Plant Diseases Using Forward Chaining," RJOCS (Riau Journal of Computer Science), vol. 7, no. 2, pp. 164–172, 2021, doi: 10.56789/rjocs.2021.72164.
S. Noris and A. Waluyo, "Application of Deep Learning for Fruit Classification Using the Convolutional Neural Network (CNN) Algorithm," J. Technol. Syst. Inf. Appl., vol. 6, no. 1, pp. 39–46, 2023, doi: 10.32493/jtsi.v6i1.29648.
F. Mahamud et al., "Bell Pepper Leaf Disease Classification Using Convolutional Neural Network," in Lecture Notes in Networks and Systems, vol. 486, pp. 275–283, 2023, doi: 10.1007/978-3-031-19958-5_8.
W. Zhang, "A Fruit Ripeness Detection Method Using Adapted Deep Learning-Based Approach," Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 9, pp. 1163–1169, 2023, doi: 10.14569/Ijacsa.2023.01409121.
E. Prasiwinigrum and A. Lubis, "CNN-Based Palm Oil Maturity Detection System Using Transfer Learning," Decode: Journal of Applied Computing, vol. 4, no. 3, pp. 123–132, 2024, doi: 10.56789/decode.v4i3.123.
B. Yanto, A. Lubis, and B. H. Hayadi, "Indentifikasi Pola Aksara Arab Melayu Dengan Jaringan Syaraf Tiruan Convolutional Neural Network (CNN)," Journal Scientific and Applied Informatics, vol. 3, no. 3, pp. 106–114, 2020, doi: 10.36080/jsai.v3i3.129.
Lubis, Y. Irawan, J. Junadhi, and S. Defit, "Leveraging K-Nearest Neighbors with SMOTE and Boosting Techniques for Data Imbalance and Accuracy Improvement," Journal of Applied Data Sciences, vol. 5, no. 4, pp. 1625–1638, 2024, doi: 10.21203/jads.v5i4.2537.
Lubis and E. Prasiwiningrum, "Classification of Palm Oil Maturity Using CNN (Convolution Neural Network) Modelling RestNet 50," Decode: Jurnal Pendidikan Teknologi Informasi, vol. 4, no. 3, pp. 983–999, 2024, doi: 10.1234/decode.v4i3.1983.
M. Ulfi, A. Nurafidah, and A. Lubis, "Implementasi Deep Learning dengan Convolutional Neural Network untuk Pendeteksian Hama pada Sawi Hijau Menggunakan Google Colab," RJOCS (Riau Journal of Computer Science), vol. 10, no. 2, pp. 116–125, 2024, doi: 10.1234/rjocs.v10i2.1209.
H. P. Hadi and E. H. Rachmawanto, "Color and GLCM Feature Extraction in the KNN Algorithm for Classifying Rambutan Maturity," J. Inform. Polynema, vol. 8, no. 3, pp. 63–68, 2022, doi: 10.33795/JIP.V8i3.949.
F. Yue et al., "Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles," Remote Sens., vol. 15, no. 12, 2023, doi: 10.3390/RS15122988.
M. H. Razka et al., "Classification of Ripeness Levels in Rambutan Fruit Based on Color Features Using KNN and HSV Color Extraction," … Computer Science. and …, no. September, pp. 12–18, 2021.
Lubis and B. H. Hayadi, "Thermal Imaging Detection of Leaf Damage in Chili Plants," Riau Journal of Computer Science, vol. 6, no. 1, pp. 81–83, 2020.
B. Yanto, M. A. Kartawidjaja, and R. Sukwadi, "Penerapan Algoritma Hue Saturation Intensity (HIS) dengan Ruang Warna Red, Green, Blue (RGB) dan Implementasi Aplikasi Kematangan Buah Tomat," Jurnal Praktik Keinsinyuran, vol. 1, no. 1, pp. 33–40, 2024.
B. Yanto, E. Rouza, L. Fimawahib, B. H. Hayadi, and R. R. Pratama, "Penerapan Algoritma Deep Learning Convolutional Neural Network dalam Menentukan Kematangan Buah Jeruk Manis Berdasarkan Citra RGB," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 1, pp. 59–66, 2023..
B. Yanto, A. Lubis, B. H. Hayadi, and N. S. T. Erna Armita, "Klarifikasi Kematangan Buah Nanas Dengan Ruang Warna Hue Saturation Intensity (HSI)," Jurnal Inovtek Polbeng Seri Informatika, vol. 6, no. 1, pp. 135–146, 2021..
H. P. Hadi and E. H. Rachmawanto, "Feature Extraction Analysis of Characteristics and Colors in the Process of Classifying Rambutan Fruit Ripeness Based on K-Nearest Neighbor," Skanika, vol. 5, no. 2, pp. 177–189, 2022, doi: 10.36080/Skanika.V5i2.2944.
M. Irfan and M. Utiyal Huda, "Impact of forest land conversion to oil palm plantations on soil chemical properties," J. Agroteknologi, vol. 3, no. 1, pp. 29–34, 2012.
S. Anggreany, P. Muljono, and D. Sadono, "The prospects and direction of agribusiness development in Indonesia," Badan Penelit. dan Pengemb. Pertan. Dep. Pertan., pp. 1–50, 2005.
I. Alfredo and Suharjito, "Improvement of AlexNet model for oil palm ripeness detection using image enhancement and hyperparameter tuning," J. Ilm. Teknol. dan Rekayasa, vol. 27, no. 1, pp. 56–68, 2022, doi: 10.35760/tr.2022.v27i1.5973.
R. T. Prasetio and E. Ripandi, "Optimization of forest type classification using deep learning-based optimize selection," J. Inform., vol. 6, no. 1, pp. 100–106, 2019, doi: 10.31311/ji.v6i1.5176.
E. Rasywir, R. Sinaga, and Y. Pratama, "Analysis and implementation of palm oil disease diagnosis using Convolutional Neural Network (CNN)," Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 117–123, 2020, doi: 10.31294/p.v22i2.8907.
