Bark recognition using novel rotationally invariant multispectral textural features

Reměs, Václav; Haindl, Michal

doi:10.1016/j.patrec.2019.06.027

Cited by 18 publications

(4 citation statements)

References 4 publications

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“…Class activation mapping(CAM) enables modifications in some parameters of CNN architecture and highlights the influential regions used for the prediction purpose. Textural features extracted through multispectral spiral wide-sense Markov model, which benefits from full descriptive colour and rotational invariance are applied for tree bark identification in [14]. The performance evaluation has been done using datasets like the BarkNet, BarkText, Trunk12 and AFF.…”

Section: IImentioning

confidence: 99%

Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

Faizal¹

2022

IJRASET

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Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it should be leaves, fruits, flowers or bark. Studies have proven that bark is of utmost importance as it will be present despite seasonal variations and provides a characteristic identity to a tree by variations in the structure. In this paper, a deep learning based approach is presented by leveraging the method of computer vision to classify 50 tree species, on the basis of bark texture using the BarkVN-50 dataset. This is the maximum number of trees being considered for bark classification till now. A convolutional neural network(CNN), ResNet101 has been implemented using transfer-learning based technique of fine tuning to maximise the model performance. The model produced an overall accuracy of >94% during the evaluation. The performance validation has been done using K-Fold Cross Validation and by testing on unseen data collected from the Internet, this proved the model's generalization capability for real-world uses.

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Section: IImentioning

confidence: 99%

Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

Faizal¹

2022

IJRASET

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“…Since the dataset is relatively large, the effectiveness of knowledge distillation slightly decreases. Remeš and Haindl (2019) proposed a texture classification method with relatively higher accuracy on a single crop (90.4%). However, the author did not mention how they…”

Section: Performance On Barknet Datasetmentioning

confidence: 99%

Deep BarkID: a portable tree bark identification system by knowledge distillation

Gazo

Beneš

et al. 2021

Eur J Forest Res

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Species identification is one of the key steps in the management and conservation planning of many forest ecosystems. We introduce Deep BarkID, a portable tree identification system that detects tree species from bark images. Existing bark identification systems rely heavily on massive computing power access, which may be scarce in many locations. Our approach is deployed as a smartphone application that does not require any connection to a database. Its intended use is in a forest, where internet connection is often unavailable. The tree bark identification is expressed as a bark image classification task, and it is implemented as a convolutional neural network (CNN). This research focuses on developing light-weight CNN models through knowledge distillation. Overall, we achieved 96.12% accuracy for tree species classification tasks for ten common tree species in Indiana, USA. We also captured and prepared thousands of bark images-a dataset that we call Indiana Bark Dataset-and we make it available at https:// github. com/ wufan you/ DBID.

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“…Boudra et al [10] introduced the Statistical Macro Binary Pattern (SMBP), a variant of the Local Binary Pattern that represents the intensity distribution within the macrostructure of large spatial support by one macro pattern code. Fekri-Ershad [11] used Local Ternary Patterns and fed them to the Multilayer Perceptron, while Remes and Haindl [12] introduced rotationally invariant multispectral textural features and reported 90.4% accuracy on BarkNet [3] using the nearest neighbor classifier.…”

Section: Introductionmentioning

confidence: 99%

CentralBark Image Dataset and Tree Species Classification Using Deep Learning

Warner,

Wu,

Gazo

et al. 2024

Algorithms

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The task of tree species classification through deep learning has been challenging for the forestry community, and the lack of standardized datasets has hindered further progress. Our work presents a solution in the form of a large bark image dataset called CentralBark, which enhances the deep learning-based tree species classification. Additionally, we have laid out an efficient and repeatable data collection protocol to assist future works in an organized manner. The dataset contains images of 25 central hardwood and Appalachian region tree species, with over 19,000 images of varying diameters, light, and moisture conditions. We tested 25 species: elm, oak, American basswood, American beech, American elm, American sycamore, bitternut hickory, black cherry, black locust, black oak, black walnut, eastern cottonwood, hackberry, honey locust, northern red oak, Ohio buckeye, Osage-orange, pignut hickory, sassafras, shagbark hickory silver maple, slippery elm, sugar maple, sweetgum, white ash, white oak, and yellow poplar. Our experiment involved testing three different models to assess the feasibility of species classification using unaltered and uncropped images during the species-classification training process. We achieved an overall accuracy of 83.21% using the EfficientNet-b3 model, which was the best of the three models (EfficientNet-b3, ResNet-50, and MobileNet-V3-small), and an average accuracy of 80.23%.

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Bark recognition using novel rotationally invariant multispectral textural features

Cited by 18 publications

References 4 publications

Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

Deep BarkID: a portable tree bark identification system by knowledge distillation

CentralBark Image Dataset and Tree Species Classification Using Deep Learning

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