{
  "id": 227295,
  "title": "🤓 Papers on herbarium and plant diversity 🤓",
  "url": "/competitions/herbarium-2021-fgvc8/discussion/227295",
  "author_name": "Charlie Craine",
  "post_date": "2021-03-19T19:42:14.452000",
  "votes": 1,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://arxiv.org/abs/1803.07892\" target=\"_blank\">Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks</a> - We are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains is promising to use this approach in future tools supporting taxonomy and natural history collection management.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2102.03213\" target=\"_blank\">A Deep Learning Approach Based on Graphs to Detect Plantation Lines</a> - This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from KEM. Experiments were conducted in corn plantations with different growth stages and patterns with aerial RGB imagery. A total of 564 patches with 256 x 256 pixels were used and randomly divided into training, validation, and testing sets in a proportion of 60\\%, 20\\%, and 20\\%, respectively. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2101.03198\" target=\"_blank\">Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset</a> - In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2102.03249\" target=\"_blank\">Spatial Functional Data Modeling of Plant Reflectances</a> - Our data are gathered for several families from the Cape Floristic Region (CFR) in South Africa. We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.</p></li>\n</ul>",
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      "id": 1245440,
      "postDate": "2021-03-19T19:42:14.453Z",
      "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://arxiv.org/abs/1803.07892\" target=\"_blank\">Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks</a> - We are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains is promising to use this approach in future tools supporting taxonomy and natural history collection management.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2102.03213\" target=\"_blank\">A Deep Learning Approach Based on Graphs to Detect Plantation Lines</a> - This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from KEM. Experiments were conducted in corn plantations with different growth stages and patterns with aerial RGB imagery. A total of 564 patches with 256 x 256 pixels were used and randomly divided into training, validation, and testing sets in a proportion of 60\\%, 20\\%, and 20\\%, respectively. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2101.03198\" target=\"_blank\">Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset</a> - In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2102.03249\" target=\"_blank\">Spatial Functional Data Modeling of Plant Reflectances</a> - Our data are gathered for several families from the Cape Floristic Region (CFR) in South Africa. We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.</p></li>\n</ul>",
      "rawMarkdown": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks](https://arxiv.org/abs/1803.07892) - We are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains is promising to use this approach in future tools supporting taxonomy and natural history collection management.\n\n- [A Deep Learning Approach Based on Graphs to Detect Plantation Lines](https://arxiv.org/abs/2102.03213) - This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from KEM. Experiments were conducted in corn plantations with different growth stages and patterns with aerial RGB imagery. A total of 564 patches with 256 x 256 pixels were used and randomly divided into training, validation, and testing sets in a proportion of 60\\%, 20\\%, and 20\\%, respectively. \n\n- [Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset](https://arxiv.org/abs/2101.03198) - In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.\n\n- [Spatial Functional Data Modeling of Plant Reflectances](https://arxiv.org/abs/2102.03249) - Our data are gathered for several families from the Cape Floristic Region (CFR) in South Africa. We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.",
      "votes": 1
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      "id": 1248441,
      "postDate": "2021-03-22T15:48:18.777Z",
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      "post_date": "2021-03-22T15:48:18.777000",
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  "raw_markdown_by_id": {
    "1245440": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks](https://arxiv.org/abs/1803.07892) - We are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains is promising to use this approach in future tools supporting taxonomy and natural history collection management.\n\n- [A Deep Learning Approach Based on Graphs to Detect Plantation Lines](https://arxiv.org/abs/2102.03213) - This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from KEM. Experiments were conducted in corn plantations with different growth stages and patterns with aerial RGB imagery. A total of 564 patches with 256 x 256 pixels were used and randomly divided into training, validation, and testing sets in a proportion of 60\\%, 20\\%, and 20\\%, respectively. \n\n- [Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset](https://arxiv.org/abs/2101.03198) - In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.\n\n- [Spatial Functional Data Modeling of Plant Reflectances](https://arxiv.org/abs/2102.03249) - Our data are gathered for several families from the Cape Floristic Region (CFR) in South Africa. We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.",
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