ADDITIONAL MENU
Development of EfficientNet Model on Broad and Needles Leaved Species Tree Crowns with Forest Health Monitoring Method
Abstract
Forest Health Monitoring (FHM) is a method for monitoring forest health conditions using various ecological indicators, such as tree canopy density and transparency. This research aims to evaluate the performance of the EfficientNet model in classifying the density and transparency values of broadleaf and coniferous tree canopies. The dataset consists of 3,956 tree canopy images collected from Tahura Wan Abdul Rachman (WAR), a conservation forest in Lampung, and is divided into 10 classes based on magic cards. Magic cards are a learning medium in the form of picture cards containing values of density and transparency. This research uses the EfficientNet-B0 architecture with certain training parameters. The results show that the EfficientNet-B0 model provides the best performance with an accuracy of 90.00%, a precision of 97.00%, a recall of 97.00%, and an F1-score of 97.00%. This research shows that EfficientNet can be used effectively to assist decision making related to automatic visual monitoring of forest health.
Keywords
Broad and Needle Leaves; Density; EfficientNet; Forest Health Monitoring; Transparency
Full Text:
PDFReferences
M. K. Haruni Krisnawati, Maarit Kallio, “Ecology , silviculture and productivity,” Cent. Int. For. Res., no. January, 2011.
R. Safe’i, Z. Nopriyanto, R. Andrian, and K. Muludi, “Implementasi Metode CNN Computer Vision Dalam Identifikasi Tipe Kerusakan Pohon Berbasis FHM,” InComTech J. Telekomun. dan Komput., vol. 13, no. 1, p. 69, 2023, doi: 10.22441/incomtech.v13i1.16022.
R. Safe’i, H. Kaskoyo, A. Darmawan, and Y. Indriani, “Kajian Kesehatan Hutan dalam Pengelolaan Hutan Konservasi,” ULIN J. Hutan Trop., vol. 4, no. 2, p. 70, 2020, doi: 10.32522/ujht.v4i2.4323.
U.S. Environmental Protection Agency, “Forest Health Monitoring - Field Methods Guide.” p. 266, 1994.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021.
P. Borugadda, R. Lakshmi, and S. Govindu, “Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models,” Curr. J. Appl. Sci. Technol., vol. 40, no. 38, pp. 29–37, 2021, doi: 10.9734/cjast/2021/v40i3831588.
A. S. Almryad and H. Kutucu, “Automatic identification for field butterflies by convolutional neural networks,” Eng. Sci. Technol. an Int. J., vol. 23, no. 1, pp. 189–195, 2020, doi: 10.1016/j.jestch.2020.01.006.
P. A. Arjun, S. Suryanarayan, R. S. Viswamanav, S. Abhishek, and T. Anjali, “Unveiling Underwater Structures: MobileNet vs. EfficientNet in Sonar Image Detection,” Procedia Comput. Sci., vol. 233, pp. 518–527, 2024, doi: 10.1016/j.procs.2024.03.241.
G. M. S. Himel, M. M. Islam, and M. Rahaman, “Utilizing EfficientNet for sheep breed identification in low-resolution images,” Syst. Soft Comput., vol. 6, no. February, p. 200093, 2024, doi: 10.1016/j.sasc.2024.200093.
M. Tan and Q. V Le, “EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks,” 2019.
F. Sofiyana, R. Andrian, and R. Safe, “MobileNet untuk Identifikasi Skala Kerapatan dan Transparansi Tajuk Pohon Daun Lebar,” vol. 4, no. 3, pp. 1850–1859, 2023, doi: 10.30865/klik.v4i3.1476.
N. A. Octarina, R. Andrian, and R. Safei, “Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16,” J. Soft Comput. Explor., vol. 4, no. 4, pp. 222–232, 2023, doi: 10.52465/joscex.v4i4.251.
F. R. Tarigan, R. Andrian, and R. Safe’i, “Klasifikasi Skala Kerapatan dan Transparansi Tajuk Jenis Daun Jarum dengan VGG16,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, pp. 253–263, 2023, doi: 10.57152/malcom.v3i2.940.
D. G. Winarno, S. P. Harianto, T. Santoso, and S. Herwanti, Taman Hutan Raya Wan Abdul Rachman Lampung, vol. 1. 2019.
M. . Ir. Yustinus Suranto, “Aspek Kualitas Kayu Dalam Konservasi dan Pemugaran Cagar Budaya Berbahan Kayu,” J. Konserv. Cagar Budaya Borobudur, vol. 06, pp. 87–93, 2012.
E. Jean-Marie, W. Jiang, D. Bereau, and J. C. Robinson, “Theobroma cacao and Theobroma grandiflorum: Botany, Composition and Pharmacological Activities of Pods and Seeds,” Foods, vol. 11, no. 24, 2022, doi: 10.3390/foods11243966.
E. Yuniastuti, N. Nandariyah, and S. R. Bukka, “Karakterisasi Durian (Durio zibenthinus) Ngrambe di Jawa Timur, Indonesia,” Caraka Tani J. Sustain. Agric., vol. 33, no. 2, p. 136, 2018, doi: 10.20961/carakatani.v33i2.19610.
O. C. Wei and S. B. A. Razak, “Rubber tree cultivation and improvement in malaysia: Anatomical and morphological studies on hevea brasiliensis and hevea camargoana,” J. Agric. Crop., vol. 7, no. 1, pp. 27–32, 2021, doi: 10.32861/jac.71.27.32.
N. Tiralla, O. Panferov, and A. Knohl, “Allometric relationships of frequently used shade tree species in cacao agroforestry systems in Sulawesi, Indonesia,” Agrofor. Syst., vol. 87, no. 4, pp. 857–870, 2013, doi: 10.1007/s10457-013-9602-4.
S. Afaq and S. Rao, “Significance Of Epochs On Training A Neural Network,” vol. 9, no. 06, pp. 485–488, 2020.
A. E. Maxwell, T. A. Warner, and L. A. Guillén, “Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review,” Remote Sens., vol. 13, no. 13, 2021, doi: 10.3390/rs13132450.
DOI: http://dx.doi.org/10.24014/ijaidm.v8i2.37463
Refbacks
- There are currently no refbacks.
Office and Secretariat:
Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau
Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942
Journal Indexing:
Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti | SINTA | Dimensions | ICI Index Copernicus
IJAIDM Stats