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Earth Science Informatics

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12.10.2022 | Research

Semantic segmentation and quantification of trees in an orchard using UAV orthophoto

verfasst von: Seyma Akca, Nizar Polat

Erschienen in: Earth Science Informatics | Ausgabe 4/2022

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Abstract

Field inspection to determine tree counts in orchards is a common practice, requiring significant time and labor. While imaging systems integrated with the Unmanned Aerial Vehicle (UAV) have provided significant opportunities in recent years, examining the images remains a daunting task. In addition, being able to comprehend the state of the tree from the pictures is a task that requires attention and experience. Deep learning algorithms have shown great potential for counting plants in UAV-derived images. This study presents a convolutional neural network (CNN) architecture for semantic segmentation of trees, shadows and soil in orchards using high resolution orthophoto produced from UAV images. In the accuracy assessment, recall, precision, IoU and F1-Score rates of the tree class were calculated as 97.02%, 87.44%, 85.15% and 91.98%, respectively. In addition, considering the land inventory, it is seen that all 475 trees in the study area are classified. It was concluded that the applied CNN architecture is an effective strategy to replace the traditional land count or visual inspection method to detect the number and location of trees in orchards.

Vorheriger Artikel Multispectral UAV and satellite images for digital soil modeling with gradient descent boosting and artificial neural network

Nächster Artikel Comparative assessment of satellite images spectral characteristics in identifying the different levels of soil salinization using machine learning techniques in Google Earth Engine

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Titel: Semantic segmentation and quantification of trees in an orchard using UAV orthophoto
verfasst von: Seyma Akca
Nizar Polat
Publikationsdatum: 12.10.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 4/2022
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-022-00871-y

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