Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2021

Идентификатор DOI: 10.3390/s21051617

Ключевые слова: deep neural networks, instance segmentation, machine learning, olive trees, ultra-high resolution images

Аннотация: Olive tree growing is an important economic activity in many countries, mostly in the Med-iterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Журнал: Sensors

Выпуск журнала: Vol. 21, Is. 5

Номера страниц: 1-17

ISSN журнала: 14248220

Издатель: MDPI AG

• Safonova Anastasiia (Siberian Fed Univ, Lab Deep Learning, Krasnoyarsk 660074, Russia; Siberian Fed Univ, Inst Space & Informat Technol, Krasnoyarsk 660074, Russia; Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18071, Spain)
• Guirado Emilio (Univ Alicante, Multidisciplinary Inst Environm Studies Ramon Mar, Alicante 03690, Spain)
• Maglinets Yuriy (Siberian Fed Univ, Inst Space & Informat Technol, Krasnoyarsk 660074, Russia)
• Alcaraz-Segura Domingo (Univ Granada, Dept Bot, Fac Sci, Granada 18071, Spain; Univ Granada, Interuniv Inst Earth Syst Res, iEcolab, Granada 18006, Spain)
• Tabik Siham (Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18071, Spain)

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