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"Trees Identification."
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Verification of a Deep Learning-Based Tree Species Identification Model Using Images of Broadleaf and Coniferous Tree Leaves
The objective of this study was to verify the accuracy of tree species identification using deep learning with leaf images of broadleaf and coniferous trees in outdoor photographs. For each of 12 broadleaf and eight coniferous tree species, we acquired 300 photographs of leaves and used those to produce 72,000 256 × 256-pixel images. We used Caffe as the deep learning framework and AlexNet and GoogLeNet as the deep learning algorithms. We constructed four learning models that combined two learning patterns: one for individual classification of 20 species and the other for two-group classification (broadleaf vs. coniferous trees), with and without data augmentation, respectively. The performance of the proposed model was evaluated according to the MCC and F-score. Both classification models exhibited very high accuracy for all learning patterns; the highest MCC was 0.997 for GoogLeNet with data augmentation. The classification accuracy was higher for broadleaf trees when the model was trained using broadleaf only; for coniferous trees, the classification accuracy was higher when the model was trained using both tree types simultaneously than when it was trained using coniferous trees only.
Journal Article
The Sibley guide to trees
\"This book covers the identification of 668 native and commonly cultivated trees found in the temperate areas of North America north of Mexico. This includes most of the continental United States and Canada, an area corresponding to the United States Department of Agriculture (USDA) plant hardiness zones 1-8\"--Introduction, p. ix.
Book
New Trees
This comprehensive volume, commissioned by the International Dendrology Society, covers more than eight hundred tree species that have been introduced to cultivation in the United Kingdom, Europe, and North America in recent decades.Up until now there has been no comparable source of information.
eBook
Practicality and Robustness of Tree Species Identification Using UAV RGB Image and Deep Learning in Temperate Forest in Japan
Identifying tree species from the air has long been desired for forest management. Recently, combination of UAV RGB image and deep learning has shown high performance for tree identification in limited conditions. In this study, we evaluated the practicality and robustness of the tree identification system using UAVs and deep learning. We sampled training and test data from three sites in temperate forests in Japan. The objective tree species ranged across 56 species, including dead trees and gaps. When we evaluated the model performance on the dataset obtained from the same time and same tree crowns as the training dataset, it yielded a Kappa score of 0.97, and 0.72, respectively, for the performance on the dataset obtained from the same time but with different tree crowns. When we evaluated the dataset obtained from different times and sites from the training dataset, which is the same condition as the practical one, the Kappa scores decreased to 0.47. Though coniferous trees and representative species of stands showed a certain stable performance regarding identification, some misclassifications occurred between: (1) trees that belong to phylogenetically close species, (2) tree species with similar leaf shapes, and (3) tree species that prefer the same environment. Furthermore, tree types such as coniferous and broadleaved or evergreen and deciduous do not always guarantee common features between the different trees belonging to the tree type. Our findings promote the practicalization of identification systems using UAV RGB images and deep learning.
Journal Article
Australian Rainforest Woods
Australian Rainforest Woods describes 141 of the most significant Australian rainforest trees and their wood. The introductory sections draw the reader into an understanding of the botanical, evolutionary, environmental, historical and international significance of this beautiful but finite Australian resource. The main section examines the species and their wood with photographs, botanical descriptions and a summary of the characteristics of the wood. A section on wood identification includes fundamental information on tree growth and wood structure, as well as images of the basic characteristics.
With more than 900 colour images, this is the most comprehensive guide ever written on Australian rainforest woods, both for the amateur and the professional wood enthusiast. It is the first time that macrophotographs of the wood have been shown in association with a physical description of wood characteristics, which will aid identification. This technique was developed by Jean-Claude Cerre, France, and his macrophotographs are included in the book.
eBook
The world of trees
A guide to more than six hundred of the world's major garden and forest trees includes coverage of the structure and life cycle of trees, how they are used in landscape design, and tree planting and care.
Book
A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data
Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.
Journal Article