Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning

• URL: http://arxiv.org/abs/2110.03994v1
• Date: Fri, 8 Oct 2021 09:25:32 GMT
• Title: Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning
• Authors: Dewald Homan (1), Johan A. du Preez (1) ((1) Faculty of Engineering, Stellenbosch University)
• Abstract summary: We present a novel and robust two-fold approach capable of identifying trees in a real-world natural setting. We leverage unlabelled data through deep semi-supervised learning and demonstrate superior performance to supervised learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species identification in the natural world. We present a novel and robust two-fold approach capable of identifying trees in a real-world natural setting. Further, we leverage unlabelled data through deep semi-supervised learning and demonstrate superior performance to supervised learning. Our single-GPU implementation for feature recognition uses minimal annotated data and achieves accuracies of 93.96% and 93.11% for leaves and bark, respectively. Further, we extract feature-specific datasets of 50 species by employing this technique. Finally, our semi-supervised species classification method attains 94.04% top-5 accuracy for leaves and 83.04% top-5 accuracy for bark.

Related papers

• Mining Field Data for Tree Species Recognition at Scale [1.264462543503282]
  We present a methodology to automatically mine species labels from public forest inventory data. We identify tree instances in aerial imagery and match them with field data with close to zero human involvement.
  arXiv  Detail & Related papers  (2024-08-28T14:25:35Z)
• PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests [0.0]
  We present the PureForest dataset: a large-scale, open, multimodal dataset designed for tree species classification. Most current public Lidar datasets for tree species classification have low diversity as they only span a small area of a few dozen annotated hectares at most. In contrast, PureForest has 18 tree species grouped into 13 semantic classes, and spans 339 km$2$ across 449 distinct monospecific forests.
  arXiv  Detail & Related papers  (2024-04-18T10:23:10Z)
• Whole-body Detection, Recognition and Identification at Altitude and Range [57.445372305202405]
  We propose an end-to-end system evaluated on diverse datasets. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios.
  arXiv  Detail & Related papers  (2023-11-09T20:20:23Z)
• Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition [30.327642724046903]
  Species196 is a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U.
  arXiv  Detail & Related papers  (2023-09-25T14:46:01Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks [0.0]
  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. In this paper, a deep learning based approach is presented by leveraging the method of computer vision to classify 50 tree species.
  arXiv  Detail & Related papers  (2022-10-03T13:09:50Z)
• Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19 [1.933681537640272]
  This paper describes a method for dissecting color images of Swedish leaves and identifying plant species. To achieve higher accuracy, the task is completed using transfer learning with the help of pre-trained classifier VGG-19. The model obtains knowledge connected to aspects of the Swedish leaf dataset, which contains fifteen tree classes, and aids in predicting the proper class of an unknown plant with an accuracy of 99.70%.
  arXiv  Detail & Related papers  (2022-09-07T11:28:50Z)
• Compositional Fine-Grained Low-Shot Learning [58.53111180904687]
  We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. We propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes.
  arXiv  Detail & Related papers  (2021-05-21T16:18:24Z)
• MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
  We present a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
  arXiv  Detail & Related papers  (2020-07-24T17:06:55Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.