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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Hernani, Livia Ayu Istoria
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Computer Science & IT

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Development of EfficientNet Model on Broad and Needles Leaved Species Tree Crowns with Forest Health Monitoring Method Hernani, Livia Ayu Istoria; Andrian, Rico; Safei, Rahmat; Tristiyanto, Tristiyanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37463

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.
Co-Authors Andrian, Rico Rahmat Safe'i Tristiyanto Tristiyanto