Implementation of deep learning based semantic segmentation method to determine vegetation density

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.265807

Keywords:

vegetation density, deep learning, semantic segmentation, classification model, two-dimensional image data

Abstract

The dryness of peatlands is influenced by the density of vegetation. If peatlands are dry, they become vulnerable to a fire risk. To calculate the drought index, professionals must conduct a vegetation density analysis. However, field analysis requires vast amounts of resources. Moreover, the accuracy of the analysis based on satellite data is not adequate. Therefore, this research presents drone-captured two-dimensional image data. The object of this research is The Liang Anggang Protection Forest Block I in Banjarbaru, South Kalimantan, Indonesia. It is surveyed for information on its vegetation cover. Afterwards, There are 300 images of vegetation cover collected and utilized in total. The method of deep learning with semantic segmentation will be used to compare the results of determining methods with expert results as ground truth. The contribution of this study is to determine the optimal performance of deep learning model used for classifying vegetation density into three categories: bare/ungrazed, lightly grazed, and heavily grazed. Performance is evaluated based on correctness and intersection over union (IoU). Obtaining the proper parameters for the classification model using deep learning techniques and comparing the results of the best segmentation model are the objectives of the following contribution. From experimental studies conducted, the optimal momentum parameter value for MobileNetV2, Xception, and Inception-ResNet-v2 is 0.9, and the optimal accuracy performance is 82.69 percent on average. The most appropriate momentum for ResNet 18 architecture is 0.1. The result of semantic segmentation using the DeepLabV3 model with Inception-ResNet-v2 architecture is the optimal model for estimating vegetation density compared to U-Net model.

Author Biographies

Yuslena Sari, Universitas Lambung Mangkurat

Master of Computer Science, Head of Department

Department of Information Technology

Yudi Arifin, Universitas Lambung Mangkurat

Doctor of Silviculture and Forest Ecology, Professor, Vice Rector for Planning, Cooperation and Public Relations

Department of Forestry

Novitasari Novitasari, Universitas Lambung Mangkurat

Doctor of Civil Engineering, Head of Hydraulic Laboratory

Department of Civil Engineering

Mohammad Faisal, Universitas Lambung Mangkurat

Doctor of Bioinformatic, Secretary of Department

Department of Computer Science

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Implementation of deep learning based semantic segmentation method to determine vegetation density

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Published

2022-10-30

How to Cite

Sari, Y., Arifin, Y., Novitasari, N., & Faisal, M. (2022). Implementation of deep learning based semantic segmentation method to determine vegetation density. Eastern-European Journal of Enterprise Technologies, 5(2(119), 42–54. https://doi.org/10.15587/1729-4061.2022.265807

Issue

Vol. 5 No. 2(119) (2022): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2022 Yuslena Sari, Yudi Arifin, Novitasari Novitasari, Mohammad Faisal

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