{
  "id": 148989,
  "title": "Research Articles with Network Architectures related to Wheat Head Detection ",
  "url": "/competitions/global-wheat-detection/discussion/148989",
  "author_name": "",
  "post_date": "2020-05-06T10:55:07.926861500Z",
  "votes": 6,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hey folks, </p>\n\n<p>Below are some useful links related to Wheat Head Detection:</p>\n\n<ol>\n<li>TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks: <a href=\"https://link.springer.com/article/10.1186/s13007-019-0537-2\">TasselNetv2</a>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F9ed36deaab96cb3b20a3b17277d614b3%2FCapture.PNG?generation=1588759410885932&amp;alt=media\" alt=\"\"></li>\n<li>Detection and analysis of wheat spikes using Convolutional Neural Networks: (<a href=\"https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8\">https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F93367cffac12766806b25d1b29c10c15%2FCapture2.PNG?generation=1588760702649507&amp;alt=media\" alt=\"\"></li>\n<li>A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting: (<a href=\"https://spj.sciencemag.org/plantphenomics/2019/1525874/\">https://spj.sciencemag.org/plantphenomics/2019/1525874/</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F3d56840fe3422b3fc55fe157ebe2ed55%2FCapture1.PNG?generation=1588759615706620&amp;alt=media\" alt=\"\"></li>\n<li>Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering:  (<a href=\"https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab\">https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F7449cc9db29a91faed53b7c1da9796ff%2F4-Figure1-1.png?generation=1588760906915967&amp;alt=media\" alt=\"\"></li>\n<li>Detection and analysis of wheat spikes using Convolutional Neural Networks: (<a href=\"https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4\">https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4</a>)</li>\n<li>Automatic Counting of Wheat Spikes from Wheat Growth Images (<a href=\"https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352\">https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352</a>)</li>\n<li>Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging: (<a href=\"https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313\">https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F77c2be3d5f13387fdecf21fa2b60a979%2F5-Figure2-111.png?generation=1588761548894203&amp;alt=media\" alt=\"\"></li>\n<li>Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods:(<a href=\"https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a\">https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a</a>)</li>\n<li>Detecting spikes of wheat plants using neural networks with Laws texture energy:(<a href=\"https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy\">https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2Fcdfaf7c2aca9a9dce9abcba83ae68a31%2FCapture111.PNG?generation=1588761965315232&amp;alt=media\" alt=\"\"></li>\n<li>Frost response in wheat and early detection using proximal sensors:(<a href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319\">https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319</a>)</li>\n</ol>",
  "messages": [
    {
      "id": "835563",
      "postDate": "05/06/2020 10:55:07",
      "content": "<p>Hey folks, </p>\n\n<p>Below are some useful links related to Wheat Head Detection:</p>\n\n<ol>\n<li>TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks: <a href=\"https://link.springer.com/article/10.1186/s13007-019-0537-2\">TasselNetv2</a>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F9ed36deaab96cb3b20a3b17277d614b3%2FCapture.PNG?generation=1588759410885932&amp;alt=media\" alt=\"\"></li>\n<li>Detection and analysis of wheat spikes using Convolutional Neural Networks: (<a href=\"https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8\">https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F93367cffac12766806b25d1b29c10c15%2FCapture2.PNG?generation=1588760702649507&amp;alt=media\" alt=\"\"></li>\n<li>A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting: (<a href=\"https://spj.sciencemag.org/plantphenomics/2019/1525874/\">https://spj.sciencemag.org/plantphenomics/2019/1525874/</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F3d56840fe3422b3fc55fe157ebe2ed55%2FCapture1.PNG?generation=1588759615706620&amp;alt=media\" alt=\"\"></li>\n<li>Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering:  (<a href=\"https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab\">https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F7449cc9db29a91faed53b7c1da9796ff%2F4-Figure1-1.png?generation=1588760906915967&amp;alt=media\" alt=\"\"></li>\n<li>Detection and analysis of wheat spikes using Convolutional Neural Networks: (<a href=\"https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4\">https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4</a>)</li>\n<li>Automatic Counting of Wheat Spikes from Wheat Growth Images (<a href=\"https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352\">https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352</a>)</li>\n<li>Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging: (<a href=\"https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313\">https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F77c2be3d5f13387fdecf21fa2b60a979%2F5-Figure2-111.png?generation=1588761548894203&amp;alt=media\" alt=\"\"></li>\n<li>Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods:(<a href=\"https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a\">https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a</a>)</li>\n<li>Detecting spikes of wheat plants using neural networks with Laws texture energy:(<a href=\"https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy\">https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy</a>)\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2Fcdfaf7c2aca9a9dce9abcba83ae68a31%2FCapture111.PNG?generation=1588761965315232&amp;alt=media\" alt=\"\"></li>\n<li>Frost response in wheat and early detection using proximal sensors:(<a href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319\">https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319</a>)</li>\n</ol>",
      "rawMarkdown": "Hey folks, \n\nBelow are some useful links related to Wheat Head Detection:\n\n1. TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks: [TasselNetv2](https://link.springer.com/article/10.1186/s13007-019-0537-2)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F9ed36deaab96cb3b20a3b17277d614b3%2FCapture.PNG?generation=1588759410885932&amp;alt=media)\n2. Detection and analysis of wheat spikes using Convolutional Neural Networks: (https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F93367cffac12766806b25d1b29c10c15%2FCapture2.PNG?generation=1588760702649507&amp;alt=media)\n3. A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting: (https://spj.sciencemag.org/plantphenomics/2019/1525874/)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F3d56840fe3422b3fc55fe157ebe2ed55%2FCapture1.PNG?generation=1588759615706620&amp;alt=media)\n4. Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering:  (https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F7449cc9db29a91faed53b7c1da9796ff%2F4-Figure1-1.png?generation=1588760906915967&amp;alt=media)\n5. Detection and analysis of wheat spikes using Convolutional Neural Networks: (https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4)\n6. Automatic Counting of Wheat Spikes from Wheat Growth Images (https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352)\n7. Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging: (https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F77c2be3d5f13387fdecf21fa2b60a979%2F5-Figure2-111.png?generation=1588761548894203&amp;alt=media)\n8. Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods:(https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a)\n9. Detecting spikes of wheat plants using neural networks with Laws texture energy:(https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2Fcdfaf7c2aca9a9dce9abcba83ae68a31%2FCapture111.PNG?generation=1588761965315232&amp;alt=media)\n10. Frost response in wheat and early detection using proximal sensors:(https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319)",
      "votes": null
    },
    {
      "id": "835564",
      "postDate": "05/06/2020 10:56:36",
      "content": "<p>Thanks for sharing !</p>\n\n<p><a href=\"https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a\">https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a</a> is our pre-print which describes the data :) !</p>",
      "rawMarkdown": "Thanks for sharing !\n\nhttps://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a is our pre-print which describes the data :) !",
      "votes": null
    },
    {
      "id": "836799",
      "postDate": "05/07/2020 08:51:33",
      "content": "<p>yeah, your paper is very informative and helpful ✌️  </p>",
      "rawMarkdown": "yeah, your paper is very informative and helpful ✌️",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 835564,
      "author_name": "bendvd",
      "author_url": "",
      "post_date": "05/06/2020 10:56:36",
      "content": "<p>Thanks for sharing !</p>\n\n<p><a href=\"https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a\">https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a</a> is our pre-print which describes the data :) !</p>",
      "votes": null,
      "replies": [
        {
          "id": 836799,
          "author_name": "spidyweb",
          "author_url": "",
          "post_date": "05/07/2020 08:51:33",
          "content": "<p>yeah, your paper is very informative and helpful ✌️  </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "835563": "Hey folks, \n\nBelow are some useful links related to Wheat Head Detection:\n\n1. TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks: [TasselNetv2](https://link.springer.com/article/10.1186/s13007-019-0537-2)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F9ed36deaab96cb3b20a3b17277d614b3%2FCapture.PNG?generation=1588759410885932&amp;alt=media)\n2. Detection and analysis of wheat spikes using Convolutional Neural Networks: (https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F93367cffac12766806b25d1b29c10c15%2FCapture2.PNG?generation=1588760702649507&amp;alt=media)\n3. A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting: (https://spj.sciencemag.org/plantphenomics/2019/1525874/)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F3d56840fe3422b3fc55fe157ebe2ed55%2FCapture1.PNG?generation=1588759615706620&amp;alt=media)\n4. Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering:  (https://www.semanticscholar.org/paper/Automated-Method-to-Determine-Two-Critical-Growth-Sadeghi-Tehran-Sabermanesh/5eb70aa1042213abfb82fc4808444b7dc080b8ab)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F7449cc9db29a91faed53b7c1da9796ff%2F4-Figure1-1.png?generation=1588760906915967&amp;alt=media)\n5. Detection and analysis of wheat spikes using Convolutional Neural Networks: (https://www.semanticscholar.org/paper/Detection-and-analysis-of-wheat-spikes-using-Neural-Hasan-Chopin/a17c4b5d899ff70e9334ef0abe55ae65768f11b4)\n6. Automatic Counting of Wheat Spikes from Wheat Growth Images (https://www.semanticscholar.org/paper/Automatic-Counting-of-Wheat-Spikes-from-Wheat-Alharbi-Zhou/a2c9157944268e87c925d00db418eaf59634f352)\n7. Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging: (https://www.semanticscholar.org/paper/Estimation-of-vegetation-indices-for-phenotyping-of-Khan-Rahimi-Eichi/c48b7b60b264ba7516d8eb2b85e503ccdd6b4313)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2F77c2be3d5f13387fdecf21fa2b60a979%2F5-Figure2-111.png?generation=1588761548894203&amp;alt=media)\n8. Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods:(https://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a)\n9. Detecting spikes of wheat plants using neural networks with Laws texture energy:(https://www.researchgate.net/publication/320385229_Detecting_spikes_of_wheat_plants_using_neural_networks_with_Laws_texture_energy)\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3588699%2Fcdfaf7c2aca9a9dce9abcba83ae68a31%2FCapture111.PNG?generation=1588761965315232&amp;alt=media)\n10. Frost response in wheat and early detection using proximal sensors:(https://onlinelibrary.wiley.com/doi/full/10.1111/jac.12319)",
    "835564": "Thanks for sharing !\n\nhttps://paperswithcode.com/paper/global-wheat-head-detection-gwhd-dataset-a is our pre-print which describes the data :) !",
    "836799": "yeah, your paper is very informative and helpful ✌️"
  },
  "source": "meta"
}