Automatic Detection of Oil Palm Growth Rate Status with YOLOv5

نویسندگان

چکیده

Oil palm plantations are essential for Indonesia as a source of foreign exchange and provider employment opportunities. However, large-scale land clearing is considered cause deforestation, which harms the environment society. So, it necessary to manage that sustainable still maintain preservation forests biodiversity. One solution apply remote sensing technology. The research was conducted develop multi-class detection method growth rate oil trees, with five categories: healthy palm, dead yellowish mismanaged smallish palm. deep learning-based object method, YOLO Version 5 (YOLOv5), used. This study compares YOLOv5 network models, namely YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x. Parameter setting also carried out in BCE (Binary Cross Entropy) Logits Loss Function handle problem unbalanced data distribution each class. model highest mAP value YOLOv5l YOLOv5x, YOLOv5x requires longer training time. In this study, hyperparameter optimization using evolution techniques. has yet provide increased results because experiments limited.

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ژورنال

عنوان ژورنال: International Journal of Advanced Computer Science and Applications

سال: 2023

ISSN: ['2158-107X', '2156-5570']

DOI: https://doi.org/10.14569/ijacsa.2023.0140361