Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16

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Nur Ayu Octarina
Rico Andrian
Rahmat Safei


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.


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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, Dec. 2023.


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