{ "id": 227297, "title": "☀️ Papers on Plant Image Detection ☀️", "url": "/competitions/plant-pathology-2021-fgvc8/discussion/227297", "author_name": "", "post_date": "2021-03-19T19:49:57.023789700Z", "votes": 8, "comment_count": 5, "views": 0, "content": "

Hey everyone!☀️

\n

I wanted to start a paper thread and build on it. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!

\n

Research Papers:

\n", "messages": [ { "id": "1245444", "postDate": "03/19/2021 19:49:57", "content": "

Hey everyone!☀️

\n

I wanted to start a paper thread and build on it. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!

\n

Research Papers:

\n", "rawMarkdown": "Hey everyone!☀️\n\nI wanted to start a paper thread and build on it. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\nResearch Papers:\n* [UP-DETR: Unsupervised Pre-training for Object Detection with Transformers](https://arxiv.org/abs/2011.09094) - The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade-off multi-task learning of classification and localization in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask.\n\n* [Strawberry Detection Using a Heterogeneous Multi-Processor Platform](https://arxiv.org/abs/2011.03651) - This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. \n\n* [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://arxiv.org/abs/2010.15821) - We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training.\n\n* [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/abs/1902.04103)\n\n* [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://arxiv.org/pdf/1906.07155.pdf)\n\n* [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/pdf/2004.10934v1.pdf)\n\n* [Cascade R-CNN: Delving into High Quality Object Detection](https://arxiv.org/abs/1712.00726)\n\n* [EfficientDet: Scalable and Efficient Object Detection](https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object)\n\n* [Cascade Mask R-CNN)CBNet: A Novel Composite Backbone Network Architecture for Object Detection](https://paperswithcode.com/paper/cbnet-a-novel-composite-backbone-network)", "votes": null }, { "id": "1246194", "postDate": "03/20/2021 14:26:16", "content": "

very nice! thank you

", "rawMarkdown": "very nice! thank you", "votes": null }, { "id": "1251337", "postDate": "03/24/2021 17:38:21", "content": "

Thanks for sharing, a few others on my list to read:

\n

Using Deep Learning for Image-Based Plant Disease Detection
\nPlant diseases and pests detection based on deep learning: a review
\nDeep Learning for Apple Diseases: Classification and Identification

", "rawMarkdown": "Thanks for sharing, a few others on my list to read: \n\nUsing Deep Learning for Image-Based Plant Disease Detection\nPlant diseases and pests detection based on deep learning: a review\nDeep Learning for Apple Diseases: Classification and Identification", "votes": null }, { "id": "1251356", "postDate": "03/24/2021 17:58:12", "content": "

@christophersubiawaud can you add links?

", "rawMarkdown": "christophersubiawaud can you add links?", "votes": null }, { "id": "1251370", "postDate": "03/24/2021 18:08:24", "content": "

Sure thing :)

\n

Deep Learning for Apple Diseases: Classification and Identification: https://arxiv.org/pdf/2007.02980.pdf

\n

Using Deep Learning for Image-Based Plant Disease Detection: https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full

\n

Plant diseases and pests detection based on deep learning: a review
\n: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00722-9

", "rawMarkdown": "Sure thing :)\n\nDeep Learning for Apple Diseases: Classification and Identification: https://arxiv.org/pdf/2007.02980.pdf\n\nUsing Deep Learning for Image-Based Plant Disease Detection: https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full\n\nPlant diseases and pests detection based on deep learning: a review\n: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00722-9", "votes": null }, { "id": "1283551", "postDate": "04/25/2021 03:32:43", "content": "

Recent Papers On AI For Agricuture 🌱

", "rawMarkdown": "[Recent Papers On AI For Agricuture 🌱](https://www.paperdigest.org/2020/09/recent-papers-on-ai-for-agriculture/)", "votes": null } ], "comments": [ { "id": 1246194, "author_name": "maxneumann", "author_url": "", "post_date": "03/20/2021 14:26:16", "content": "

very nice! thank you

", "votes": null, "replies": [] }, { "id": 1251337, "author_name": "christophersubiawaud", "author_url": "", "post_date": "03/24/2021 17:38:21", "content": "

Thanks for sharing, a few others on my list to read:

\n

Using Deep Learning for Image-Based Plant Disease Detection
\nPlant diseases and pests detection based on deep learning: a review
\nDeep Learning for Apple Diseases: Classification and Identification

", "votes": null, "replies": [ { "id": 1251356, "author_name": "crained", "author_url": "", "post_date": "03/24/2021 17:58:12", "content": "

@christophersubiawaud can you add links?

", "votes": null, "replies": [] }, { "id": 1251370, "author_name": "christophersubiawaud", "author_url": "", "post_date": "03/24/2021 18:08:24", "content": "

Sure thing :)

\n

Deep Learning for Apple Diseases: Classification and Identification: https://arxiv.org/pdf/2007.02980.pdf

\n

Using Deep Learning for Image-Based Plant Disease Detection: https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full

\n

Plant diseases and pests detection based on deep learning: a review
\n: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00722-9

", "votes": null, "replies": [] } ] }, { "id": 1283551, "author_name": "aithammadiabdellatif", "author_url": "", "post_date": "04/25/2021 03:32:43", "content": "

Recent Papers On AI For Agricuture 🌱

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1245444": "Hey everyone!☀️\n\nI wanted to start a paper thread and build on it. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\nResearch Papers:\n* [UP-DETR: Unsupervised Pre-training for Object Detection with Transformers](https://arxiv.org/abs/2011.09094) - The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade-off multi-task learning of classification and localization in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask.\n\n* [Strawberry Detection Using a Heterogeneous Multi-Processor Platform](https://arxiv.org/abs/2011.03651) - This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. \n\n* [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://arxiv.org/abs/2010.15821) - We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training.\n\n* [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/abs/1902.04103)\n\n* [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://arxiv.org/pdf/1906.07155.pdf)\n\n* [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/pdf/2004.10934v1.pdf)\n\n* [Cascade R-CNN: Delving into High Quality Object Detection](https://arxiv.org/abs/1712.00726)\n\n* [EfficientDet: Scalable and Efficient Object Detection](https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object)\n\n* [Cascade Mask R-CNN)CBNet: A Novel Composite Backbone Network Architecture for Object Detection](https://paperswithcode.com/paper/cbnet-a-novel-composite-backbone-network)", "1246194": "very nice! thank you", "1251337": "Thanks for sharing, a few others on my list to read: \n\nUsing Deep Learning for Image-Based Plant Disease Detection\nPlant diseases and pests detection based on deep learning: a review\nDeep Learning for Apple Diseases: Classification and Identification", "1251356": "christophersubiawaud can you add links?", "1251370": "Sure thing :)\n\nDeep Learning for Apple Diseases: Classification and Identification: https://arxiv.org/pdf/2007.02980.pdf\n\nUsing Deep Learning for Image-Based Plant Disease Detection: https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full\n\nPlant diseases and pests detection based on deep learning: a review\n: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00722-9", "1283551": "[Recent Papers On AI For Agricuture 🌱](https://www.paperdigest.org/2020/09/recent-papers-on-ai-for-agriculture/)" }, "source": "meta" }