Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16
Main Article Content
Abstract
Crown density and foliage transparency are important parameters for tree crown conditions. Previously, observers carried out crown density and foliage transparency assessments manually, which could be a less efficient process.This research aims to use the VGG-16 deep learning architecture to classify the density and transparency of broadleaves tree crowns. In this study, broadleaves tree crown datasets were collected for four types of broadleaves tree: cacao (theobroma cacao), durian (durio zibethinus), rubber (havea brasiliensis), candlenut (aleurites moluccana); then the data is labeled based on the crown density and foliage transparency scale card. The research applies resize and augmentation preprocessing. The model training process uses a scenario of 80% train data, 10% test data, and 10% validation data. After training using the VGG-16 model, the test results showed impressive accuracy, with the highest accuracy reaching 98.40% for candlenut trees, rubber (96.00%), cacao (92.00%), and durian (86.60%). This research shows quite good results in classifying the scale of crown density and foliage transparency with four types of broadleaves tree (cacao, durian, rubber and candlenut) using VGG-16.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
R. Safe’i, H. Kaskoyo, A. Darmawan, and Y. Indriani, “Kajian Kesehatan Hutan Dalam Pengelolaan Hutan Konservasi,” Hut Trop, vol. 4, no. 2, pp. 70–76, 2020.
A. Feriansyah, R. Safe’i, A. Darmawan, and H. Kaskoyo, “Status Kesehatan Hutan Berdasarkan Indikator Kondisi Tajuk (Studi Pada Tiga Fungsi Hutan di Provinsi Lampung),” in Konservasi Sumber Daya Alam Untuk Pembangunan Berkelanjutan, Bandar Lampung: LPPM Universitas Lampung, 2020, pp. 243–249.
L. Pratiwi and R. Safe’i, “Penilaian Vitalitas Pohon Jati dengan Forest Health Monitoring di KPH Balapulang,” Jurnal EcoGreen:Journal of Forestry and Enviromental Science, vol. 4, no. 1, pp. 9–15, 2018.
A. Feriansyah, R. Safe’i, A. Darmawan, and H. Kaskoyo, “Comparison of Crown Health Assessment Using Forest Health Monitoring and Remote Sensing Techniques (Case Study: KTH Lestari Jaya 8, KPHL Kota Agung Utara, Lampung),” IOP Conference Series: Earth and Environmental Science, vol. 1115, no. 1, 2022, doi: 10.1088/1755-1315/1115/1/012055.
Supriyanto and T. Iskandar, “Penilaian Kesehatan Kebun Benih Semai Pinus Merkusii Dengan Metode FHM (Forest Health Monitoring) di KPH Sumedang,” Journal of Tropical Silviculture, vol. 9, no. 2, 2019, doi: 10.29244/j-siltrop.9.2.99-108.
S. A. Paembonan, Silvika Ekofisiologi dan Pertumbuhan Pohon, 1st ed. Makassar: Fakultas Kehutanan Universitas Hasanuddin, 2020.
B. M. Abuhayi and A. A. Mossa, “Coffee disease classification using Convolutional Neural Network based on feature concatenation,” Inform Med Unlocked, vol. 39, p. 101245, 2023, doi: 10.1016/j.imu.2023.101245.
B. Prasetiyo, Alamsyah, M. F. Al Hakim, Jumanto, and M. H. Adi, “Differential augmentation data for vehicle classification using convolutional neural network,” 2023, p. 040001. doi: 10.1063/5.0126720.
A. A. Hakim, E. Juanara, and R. Rispandi, “Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano,” Journal of Information System Exploration and Research, vol. 1, no. 2, Jul. 2023, doi: 10.52465/joiser.v1i2.175.
P. Pampouktsi et al., “Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector,” Journal of Information System Exploration and Research, vol. 1, no. 1, pp. 1–16, Dec. 2022, doi: 10.52465/joiser.v1i1.91.
I. U. Haq, H. Ali, H. Y. Wang, C. Lei, and H. Ali, “Feature fusion and Ensemble learning-based CNN model for mammographic image classification,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3310–3318, Jun. 2022, doi: 10.1016/j.jksuci.2022.03.023.
H. Syamsudin, S. Khalidah, and J. Unjung, “Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 7, no. 1, Nov. 2023, doi: 10.24014/ijaidm.v7i1.24586.
X. Wu et al., “CTransCNN: Combining transformer and CNN in multilabel medical image classification,” Knowl Based Syst, vol. 281, p. 111030, Dec. 2023, doi: 10.1016/j.knosys.2023.111030.
L. Alzubaidi et al., “Review of deep learning : concepts , CNN architectures , challenges , applications , future directions,” J Big Data, pp. 1–74, 2021, doi: 10.1186/s40537-021-00444-8.
G. S. Nijaguna, J. A. Babu, B. D. Parameshachari, R. P. de Prado, and J. Frnda, “Quantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis,” Appl Soft Comput, vol. 136, p. 110055, Mar. 2023, doi: 10.1016/j.asoc.2023.110055.
S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” International Journal of Scientific and Research Publication, vol. 9, no. 10, 2019, doi: 10.29322/IJSRP.9.10.2019.p9420.
W. L. Pratitis, Kursini, and H. Al Fata, “Classification of Spotted Disease on Sugarcane Leaf Image Using Convolutional Neural Network Algorithm,” JTECS: Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem & Komputer, vol. 3, no. 1, pp. 117–128, 2023, doi: 10.32503/jtecs.v3i2.3433.
M. A. Hasan, Y. Riyanto, and D. Riana, “Klasifikasi penyakit citra daun anggur menggunakan model CNN-VGG16,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 4, pp. 218–223, 2021, doi: 10.14710/jtsiskom.2021.14013.
R. Windiawan and A. Suharso, “Identifikasi Penyakit pada Daun Kopi Menggunakan Metode Deep Learning,” ExploreIT, vol. 5, no. 36, pp. 9–16, 2021, doi: https://doi.org/10.35891/explorit.v13i2.2689.
M. Rezaee, Y. Zhang, and R. Mishra, “Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach,” in 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China: IEEE, 2018, pp. 1–7. doi: 10.1109/PRRS.2018.8486395.
D. Pertiwi, R. Safe’i, and H. Kaskoyo, “Forest Health at Wan Abdul Rachman Forest Park Lampung Province,” Jurnal Hutan Tropis, vol. 8, no. 3, pp. 251–259, 2020.
L. de L. Saavedra-Romero, D. Alvarado-Rosales, P. H. La Rosa, T. Martínez-Trinidad, G. Mora-Aguilera, and J. Villa-Castillo, “Canopy condition: Health indicator of urban trees in the San Juan de Aragón Forest, Mexico City,” Sustainable Forestry, vol. 5, no. 1, p. 85, 2022, doi: 10.24294/sf.v5i1.1623.
R. Safe’i, A. Darmawan, H. Kaskoyo, and C. F. G. Rezinda, “Analysis of Changes in Forest Health Status Values in Conservation Forest (Case Study: Plant and Animal Collection Blocks in Wan Abdul Rachman Forest Park (Tahura WAR)),” Journal of Physics: Conference Series, vol. 1842, no. 1, 2021, doi: 10.1088/1742-6596/1842/1/012049.
D. Murcia-gómez, I. Rojas-valenzuela, and O. Valenzuela, “Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images,” Appl. Sci., vol. 12, p. 11375, 2022, doi: 10.3390/app122211375.
C. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, p. 60, 2019, doi: 10.1186/s40537-019-0197-0.
A. Julianto, A. Sunyoto, and F. W. Wibowo, “Optimasi Hyperparameter Convolutional Neural Network untuk Klasifikasi Penyakit Tanaman Padi,” Teknimedia, vol. 3, no. 2, 2022.
S. Shafi and A. Assad, “Exploring the Relationship Between Learning Rate, Batch Size, and Epochs in Deep Learning: An Experimental Study,” in Soft Computing for Problem Solving, M. Thakur, S. Agnihotri, B. S. Rajpurohit, M. Pant, K. Deep, and A. K. Nagar, Eds., Singapore: Springer Nature Singapore, 2023, pp. 201–209.
I. Kandel, M. Castelli, and L. Manzoni, “Brightness as an Augmentation Technique for Image Classification,” Emerging Science Journal, vol. 6, no. 4, pp. 881–892, 2022, doi: /10.28991/ESJ-2022-06-04-015.
H. C. Chen et al., “AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf,” Electronics (Switzerland), vol. 11, no. 6, pp. 1–17, 2022, doi: 10.3390/electronics11060951.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 2015, doi: /10.48550/arXiv.1409.1556.
P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) pada Ekspresi Manusia,” Jurnal Algor, vol. 2, no. 1, pp. 12–21, 2020.
J. N. Mogan, C. P. Lee, K. M. Lim, and K. S. Muthu, “VGG16-MLP : Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron,” Appl. Sci., vol. 12, p. 7639, 2022, doi: /10.3390/app12157639.
M. E. H, R. N. Wijaya, and H. K. Ahsan, “Enhancing cirrhosis detection : A deep learning approach with convolutional neural networks,” Journal of Soft Computing Exploration, vol. 4, no. 4, pp. 196–205, 2023, doi: /10.52465/joscex.v4i3.2.
T. Sulistyowati, Purwanto, F. Alzami, and R. A. Pramunendar, “VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases,” Moneter: Jurnal Keuangan dan Perbankan, vol. 11, no. 1, pp. 41–53, 2023, doi: 10.32832/moneter.v11i1.57.
Ž. Vujović, “Classification Model Evaluation Metrics,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 599–606, 2021, doi: 10.14569/IJACSA.2021.0120670.