{"name":"deepphenotree","display_name":"DeepPhenoTree","visibility":"public","icon":"","categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"deepphenotree.get_reader","title":"Open data with DeepPhenoTree","python_name":"deepphenotree._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"deepphenotree.make_sample_data","title":"Load sample data from DeepPhenoTree","python_name":"deepphenotree._sample_data:make_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"deepphenotree.three_widget","title":"Three Buttons Widget","python_name":"deepphenotree:ThreeButtonsWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"deepphenotree.load_fruit_switzerland","title":"Load Fruit 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belgium"},{"command":"deepphenotree.load_fruitlet_spain","key":"fruitlet3","display_name":"Fruitlet spain"},{"command":"deepphenotree.load_fruitlet_italy","key":"fruitlet4","display_name":"Fruitlet italy"},{"command":"deepphenotree.load_flowering_switzerland","key":"flowering1","display_name":"Flowering switzerland"},{"command":"deepphenotree.load_flowering_belgium","key":"flowering2","display_name":"Flowering belgium"},{"command":"deepphenotree.load_flowering_spain","key":"flowering3","display_name":"Flowering spain"},{"command":"deepphenotree.load_flowering_italy","key":"flowering4","display_name":"Flowering italy"}],"themes":null,"menus":{"napari/layers/segment":[{"command":"deepphenotree.make_container_widget","when":null,"group":null,"alt":null},{"command":"deepphenotree.make_magic_widget","when":null,"group":null,"alt":null},{"command":"deepphenotree.make_function_widget","when":null,"group":null,"alt":null},{"command":"deepphenotree.make_qwidget","when":null,"group":null,"alt":null}]},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"deepphenotree","version":"1.0.8","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"A simple plugin to use models developped for flowering, fruitlet and fruit","description":"# DeepPhenoTree\n\n[![License GNU LGPL v3.0](https://img.shields.io/pypi/l/deepphenotree.svg?color=green)](https://github.com/hereariim/deepphenotree/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/deepphenotree.svg?color=green)](https://pypi.org/project/deepphenotree)\n[![Python Version](https://img.shields.io/pypi/pyversions/deepphenotree.svg?color=green)](https://python.org)\n[![tests](https://github.com/hereariim/deepphenotree/workflows/tests/badge.svg)](https://github.com/hereariim/deepphenotree/actions)\n[![codecov](https://codecov.io/gh/hereariim/deepphenotree/branch/main/graph/badge.svg)](https://codecov.io/gh/hereariim/deepphenotree)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/deepphenotree)](https://napari-hub.org/plugins/deepphenotree)\n[![npe2](https://img.shields.io/badge/plugin-npe2-blue?link=https://napari.org/stable/plugins/index.html)](https://napari.org/stable/plugins/index.html)\n[![Copier](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/copier-org/copier/master/img/badge/badge-grayscale-inverted-border-purple.json)](https://github.com/copier-org/copier)\n\n\nHerearii Metuarea, Abdoul-Djalil Ousseini Hamza, Walter Guerra†, Andrea Patocchi, Lidia Lozano, Shauny Van Hoye, Francois Laurens, Jeremy Labrosse, Pejman Rasti, David Rousseau†.\n\n† project lead\n\n\"Capture\n\n\"DeepPhenoTreeTools\"\n\nDeepPhenoTree is though as a tool to enable automatic detection of phenological stages associated with flowering, fruitlet, and fruit in harvest time from images using deep learning–based object detection models.\n\nThis [napari] plugin was generated with [copier] using the [napari-plugin-template] (None).\n\n### Contribution\n\n### Article (under review)\n\n*DeepPhenoTree – Apple Edition: a Multi-site apple phenology RGB annotated dataset with deep learning baseline models.*\nHerearii Metuarea, Abdoul djalil Ousseni hamza, Walter Guerra, Andrea Patocchi, Lidia Lozano, Shauny Van Hoye, Francois Laurens, Jeremy Labrosse, Pejman Rasti, David Rousseau.\n\n### Dataset\n\nHerearii Metuarea; Abdoul djalil Ousseni hamza; Lou Decastro; Jade Marhadour; Oumaima Karia; Lorène Masson; Marie Kourkoumelis-Rodostamos; Walter Guerra; Francesca Zuffa; Francesco Panzeri; Andrea Patocchi; Lidia Lozano; Shauny Van Hoye; Marijn Rymenants; François Laurens; Jeremy Labrosse; Pejman Rasti; David Rousseau, 2026, \"DeepPhenoTree - Apple Edition\", https://doi.org/10.57745/NORPF1, Recherche Data Gouv, V5, UNF:6:FyJNuJx4BVZxWuG8hI4gEw== [fileUNF]\n\n----------------------------------\n\n## Installation\n\nYou can install `deepphenotree` via [pip]:\n\n```\npip install deepphenotree\n```\n\nIf napari is not already installed, you can install `deepphenotree` with **napari** and **Qt** via:\n\n```\npip install \"deepphenotree[all]\"\n```\n\nTo install latest development version :\n\n```\npip install git+https://github.com/hereariim/deepphenotree.git\n```\n\nGPU is mandatory for time processing and models running (especially RT-DETR). Please visit the official PyTorch website to get the appropriate installation command:\n👉 https://pytorch.org/get-started/locally\n\n**Exemple : GPU (CUDA 12.1)**\n\n pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n\n## Getting started\n\n### Running from Python\n\n#### 1. Load sample image\n\n```\nfrom deepphenotree._sample_data import DeepPhenoTreeData\n\n# Flowering data\ndata_flower = DeepPhenoTreeData('Flowering')\nimages = data_flower.data # Dimension : (5120, 5120, 3, 4)\ncountry = data_flower.names # ['Belgium', 'Italy', 'Spain', 'Switzerland']\n\n# Fruitlet data\ndata_fruitlet = DeepPhenoTreeData('Fruitlet')\n# Fruit data\ndata_fruit = DeepPhenoTreeData('Fruit')\n```\n\n#### 2. Run inference\n\n```\nfrom deepphenotree.inference import YoloInferencer\nimage = # Your RGB image\n\n# Flowering task\ninfer = YoloInferencer(\"Flowering\")\nbbx = infer.predict_boxes(image)\n\n# Fruitlet task\ninfer = YoloInferencer(\"Fruitlet\")\nbbx = infer.predict_boxes(image)\n\n# Fruit task\ninfer = YoloInferencer(\"Fruit\")\nbbx = infer.predict_boxes(image)\n```\n\n### Running from Napari\n\nThis plugin is a tool to perform targeted image inference on user-provided images. Users can run three specific detection tasks via dedicated buttons: flowering, fruitlet, and fruit detection. The plugin returns the coordinates of bounding boxes around detected objects, and a message informs the user of the number of detected boxes. Several developments are ongoing—feel free to contact us if you have requests or suggestions.\n\n\n\n\"Deepphenotree_plugin\"\n\n### Scheme\n\n\"User_tool\"\n\n### Input\n\nUser drag and drop RGB image on napari window. Otherwise, user can select an image among suggested images from the plugin :\n\nFile > Open Sample > DeepPhenoTree > images\n\nNote : The images available in Open Sample > DeepPhenoTree correspond to the test data associated with the models provided in this plugin.\n\n### Process\n\nUser click to make inference in image : \n- Flowering : Detect all objects (from BBCH 00 to BBCH 69) from bud developpement to flowering.\n- Fruitlet : Detect fruit in developement (from BBCH 71 to 77)\n- Fruit : Detect all fruit in harvest time (from BBCH 81 to 89)\n\n### Output\n\nBounding box displayed in layer Flowering for flowering, Fruitlet for fruitlet and Fruit for fruit.\n\n## Model\n\nDeepPhenoTree consists of a RT-DETR trained on DeepPhenoTree dataset. \n\nThe trained models used in this project are **not publicly available**. They are part of ongoing research and collaborative projects, and therefore cannot be distributed at this time. \nHowever, the codebase is provided to ensure **reproducibility** and **transparency** of the proposed methodology.\n\n### Images results\n\n**Standard deviation is computed over 5-fold cross-validation. Overall (4 sites) denotes the aggregated evaluation across the four experimental sites (Switzerland, Belgium, Spain, and Italy).**\n\n| Dataset | Location | Precision | Recall | mAP@.5 | mAP@.5:.95 |\n| ------------ | ---------------------- | ---------------- | ---------------- | -------------- | -------------- |\n| | Overall (4 sites) | 0.69 ± 0.01 | 0.58 ± 0.02 | 0.65 ± 0.02 | 0.37 ± 0.02 |\n| | Switzerland | 0.73 ± 0.02 | 0.60 ± 0.04 | 0.68 ± 0.03 | 0.40 ± 0.04 |\n| Flowering | Belgium | 0.72 ± 0.02 | 0.63 ± 0.03 | 0.69 ± 0.03 | 0.40 ± 0.03 |\n| | Spain | 0.66 ± 0.01 | 0.53 ± 0.05 | 0.60 ± 0.03 | 0.30 ± 0.02 |\n| | Italy | 0.69 ± 0.04 | 0.61 ± 0.03 | 0.67 ± 0.04 | 0.40 ± 0.04 |\n| ------------ | ---------------------- | ---------------- | ---------------- | -------------- | -------------- |\n| | Overall (4 sites) | 0.85 ± 0.02 | 0.73 ± 0.02 | 0.82 ± 0.02 | 0.53 ± 0.01 |\n| | Switzerland | 0.86 ± 0.04 | 0.78 ± 0.04 | 0.84 ± 0.06 | 0.56 ± 0.04 |\n| Fruitlet | Belgium | 0.83 ± 0.03 | 0.65 ± 0.04 | 0.77 ± 0.04 | 0.52 ± 0.14 |\n| | Spain | 0.86 ± 0.02 | 0.72 ± 0.03 | 0.81 ± 0.03 | 0.52 ± 0.03 |\n| | Italy | 0.88 ± 0.01 | 0.80 ± 0.01 | 0.88 ± 0.01 | 0.61 ± 0.01 |\n| ------------ | ---------------------- | ---------------- | ---------------- | -------------- | -------------- |\n| | Overall (4 sites) | 0.87 ± 0.01 | 0.79 ± 0.01 | 0.86 ± 0.01 | 0.57 ± 0.01 |\n| | Switzerland | 0.86 ± 0.03 | 0.80 ± 0.02 | 0.87 ± 0.02 | 0.59 ± 0.01 |\n| Fruit | Belgium | 0.90 ± 0.01 | 0.84 ± 0.01 | 0.90 ± 0.01 | 0.63 ± 0.02 |\n| | Spain | 0.86 ± 0.02 | 0.75 ± 0.02 | 0.84 ± 0.02 | 0.51 ± 0.03 |\n| | Italy | 0.88 ± 0.02 | 0.84 ± 0.03 | 0.90 ± 0.02 | 0.66 ± 0.02 |\n\n\n## DeepPhenoTree Dataset\n\nDeepPhenoTree – Apple Edition, a multi-site, multi-variety, RGB image dataset dedicated to the classification of key apple treephenological stages.\n\n\"IRTA_time\"\n\n## Acknowlegments\n\nThis work is led by IRHS, Université d'Angers, INRAe, Institut Agro. It is supported by :\n\n- The European Union’s Horizon Europe research innovation programme under grant agreement No 101094587 ([PHENET project](https://doi.org/10.3030/101094587))\n- The French National Research Agency (ANR) as part of the Programme d'Investissements d'Avenir under grant agreement ANR-11-INBS-0012 ([PHENOME-EMPHASIS](https://anr.fr/ProjetIA-11-INBS-0012))\n- The French National Research Agency (ANR) as part of the France 2030 investment plan under grant agreement ANR-22-PEAE-0012 ([AGROECOPHEN](https://anr.fr/ProjetIA-22-PEAE-0012))\n- The HPC resources of IDRIS under the allocation 2024-AD010115553 made by GENCI.\n\n\n## Contact\n\nImhorphen team, bioimaging research group\n42 rue George Morel, Angers, France\n\n- Herearii Metuarea, herearii.metuarea@univ-angers.fr\n- Abdoul-Djalil Ousseini Hamza, abdoul-djalil.ousseini-hamza@inrae.fr\n- Pr David Rousseau, david.rousseau@univ-angers.fr\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [GNU LGPL v3.0] license,\n\"deepphenotree\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n## Citing\n\nIf you use DeepPhenoTree in your research, please use the following BibTeX entry.\n\n```\n@article{metuarea2026deepphenotree,\n title={DeepPhenoTree--Apple Edition: a Multi-site apple phenology RGB annotated dataset with deep learning baseline models},\n author={Herearii Metuarea and Abdoul-Djalil Ousseini Hamza and Walter Guerra and Francesca Zuffa and Francesco Panzeri and Andrea Patocchi and Lidia Lozano and Shauny Van Hoye and François Laurens and Jeremy Labrosse and Pejman Rasti and David Rousseau},\n year={2026}\n}\n```\n\nIf you use DeepPhenoTree dataset in your research, please use the following BibTeX entry.\n\n```\n@dataset{metuarea2026deepphenotree_dataset,\n author = {Metuarea Herearii and Abdoul-Djalil Ousseini Hamza and Decastro Lou and Marhadour Jade and Karia Oumaima and Masson Lorène and Kourkoumelis-Rodostamos Marie and Guerra Walter and Zuffa Francesca and Panzeri Francesco and Patocchi Andrea and Lozano Lidia and Van Hoye Shauny and Rymenants Marijn and Laurens François and Labrosse Jeremy and Rasti Pejman and Rousseau David},\n title = {DeepPhenoTree – Apple Edition},\n year = {2026},\n version = {5},\n publisher = {Recherche Data Gouv},\n doi = {10.57745/NORPF1}\n}\n```\n\n\n[napari]: https://github.com/napari/napari\n[copier]: https://copier.readthedocs.io/en/stable/\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[napari-plugin-template]: https://github.com/napari/napari-plugin-template\n\n[file an issue]: https://github.com/hereariim/deepphenotree/issues\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Herearii Metuarea, Abdoul djalil Ousseini Hamza, Walter Guerra, Andrea Patocchi, Lidia Lozano, Shauny Van Hoye, Francois Laurens, Jeremy Labrosse, Pejman Rasti, David Rousseau","author_email":"herearii.metuarea@gmail.com","maintainer":null,"maintainer_email":null,"license":"GNU LESSER GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n\n This version of the GNU Lesser General Public License incorporates\nthe terms and conditions of version 3 of the GNU General Public\nLicense, supplemented by the additional permissions listed below.\n\n 0. Additional Definitions.\n\n As used herein, \"this License\" refers to version 3 of the GNU Lesser\nGeneral Public License, and the \"GNU GPL\" refers to version 3 of the GNU\nGeneral Public License.\n\n \"The Library\" refers to a covered work governed by this License,\nother than an Application or a Combined Work as defined below.\n\n An \"Application\" is any work that makes use of an interface provided\nby the Library, but which is not otherwise based on the Library.\nDefining a subclass of a class defined by the Library is deemed a mode\nof using an interface provided by the Library.\n\n A \"Combined Work\" is a work produced by combining or linking an\nApplication with the Library. 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