Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean 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.

中文翻译：

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使用 Mask R-CNN 进行无人机多分辨率图像分割的实时树木生物量


橄榄树种植是许多国家的一项重要经济活动，主要集中在地中海盆地、阿根廷、智利、澳大利亚和加利福尼亚州。尽管最近的集约化技术将橄榄园组织在树篱中，但大多数橄榄园都是靠雨养的，而且树木分散（如西班牙和意大利，它们占世界橄榄油产量的 50%）。准确测量树木的生物体积是监测其橄榄生产和健康表现的第一步。在这项工作中，我们使用最准确的深度学习实例分割方法之一（Mask R-CNN）和无人机（UAV）图像进行橄榄树树冠和阴影分割（OTCS），以进一步估计单棵树的生物体积。我们对具有不同光谱带（红、绿、蓝和近红外）和植被指数（归一化植被指数 - NDVI - 和绿色归一化植被指数 - GNDVI）的图像评估了我们的方法。红绿蓝 (RGB) 图像的性能在 3 厘米/像素和 13 厘米/像素两种空间分辨率下进行评估，而 NDVI 和 GNDV 图像仅为 13 厘米/像素。所有经过训练的基于 Mask R-CNN 的模型在树冠分割方面都表现出高性能，特别是在融合 GNDVI 和 NDVI 中的所有数据集时（F1 测量从 95% 到 98%）。我们估计的树木子集生物体积与地面真实测量值的比较表明，平均准确度为 82%。我们的结果支持使用 NDVI 和 GNDVI 光谱指数来准确估计无人机图像中分散树木（例如橄榄树）的生物体积。