[{"data":1,"prerenderedAt":240},["ShallowReactive",2],{"research-list-de":3},[4,162],{"id":5,"title":6,"authors":7,"body":8,"category":151,"conference":152,"date":153,"description":41,"extension":154,"image":155,"meta":156,"navigation":157,"path":158,"seo":159,"stem":160,"__hash__":161},"research\u002Fde\u002Fresearch\u002F20240929_eccv.md","Deep Learning Based Growth Modeling of Plant Phenotypes","Renke Hohl, Moritz Schauer, Seyed Eghbal Ghobadi",{"type":9,"value":10,"toc":147},"minimark",[11,16,20,31,35,143],[12,13,15],"h2",{"id":14},"abstract","Abstract",[17,18,19],"p",{},"This paper investigates the less explored domain of imagebased plant growth modeling using deep learning. Our approach aims to model plant phenotypes at different growth stages, laying the foundation for further research on generating synthethic training data for object detection and segmentation We introduce a novel class of neural network architectures called Latent Plant Growth Models (LPGMs), which model the temporal aspects of plant growth within the latent space of pre-trained autoencoders. Two distinct LPGM architectures have been developed and investigated. These architectures demonstrate improved performance over existing state-of-the-art methods in terms of image quality and training time, while retaining a high degree of realism in the simulated growth.",[17,21,22,23],{},"Read More: ",[24,25,30],"a",{"href":26,"rel":27,"target":29},"https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-031-91835-3_15",[28],"nofollow","_blank","ECCV 2024 Proceedings",[12,32,34],{"id":33},"cite-as","Cite As",[36,37,42],"pre",{"className":38,"code":39,"language":40,"meta":41,"style":41},"language-latex shiki shiki-themes github-light github-dark","@InProceedings{10.1007\u002F978-3-031-91835-3_15,\n    author=\"Hohl, Renke\n    and Schauer, Moritz\n    and Ghobadi, Seyed Eghbal\",\n    editor=\"Del Bue, Alessio\n    and Canton, Cristian\n    and Pont-Tuset, Jordi\n    and Tommasi, Tatiana\",\n    title=\"Deep Learning Based Growth Modeling of Plant Phenotypes\",\n    booktitle=\"Computer Vision -- ECCV 2024 Workshops\",\n    year=\"2025\",\n    publisher=\"Springer Nature Switzerland\",\n    address=\"Cham\",\n    pages=\"224--239\",\n    isbn=\"978-3-031-91835-3\"\n}\n","latex","",[43,44,45,53,59,65,71,77,83,89,95,101,107,113,119,125,131,137],"code",{"__ignoreMap":41},[46,47,50],"span",{"class":48,"line":49},"line",1,[46,51,52],{},"@InProceedings{10.1007\u002F978-3-031-91835-3_15,\n",[46,54,56],{"class":48,"line":55},2,[46,57,58],{},"    author=\"Hohl, Renke\n",[46,60,62],{"class":48,"line":61},3,[46,63,64],{},"    and Schauer, Moritz\n",[46,66,68],{"class":48,"line":67},4,[46,69,70],{},"    and Ghobadi, Seyed Eghbal\",\n",[46,72,74],{"class":48,"line":73},5,[46,75,76],{},"    editor=\"Del Bue, Alessio\n",[46,78,80],{"class":48,"line":79},6,[46,81,82],{},"    and Canton, Cristian\n",[46,84,86],{"class":48,"line":85},7,[46,87,88],{},"    and Pont-Tuset, Jordi\n",[46,90,92],{"class":48,"line":91},8,[46,93,94],{},"    and Tommasi, Tatiana\",\n",[46,96,98],{"class":48,"line":97},9,[46,99,100],{},"    title=\"Deep Learning Based Growth Modeling of Plant Phenotypes\",\n",[46,102,104],{"class":48,"line":103},10,[46,105,106],{},"    booktitle=\"Computer Vision -- ECCV 2024 Workshops\",\n",[46,108,110],{"class":48,"line":109},11,[46,111,112],{},"    year=\"2025\",\n",[46,114,116],{"class":48,"line":115},12,[46,117,118],{},"    publisher=\"Springer Nature Switzerland\",\n",[46,120,122],{"class":48,"line":121},13,[46,123,124],{},"    address=\"Cham\",\n",[46,126,128],{"class":48,"line":127},14,[46,129,130],{},"    pages=\"224--239\",\n",[46,132,134],{"class":48,"line":133},15,[46,135,136],{},"    isbn=\"978-3-031-91835-3\"\n",[46,138,140],{"class":48,"line":139},16,[46,141,142],{},"}\n",[144,145,146],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":41,"searchDepth":55,"depth":55,"links":148},[149,150],{"id":14,"depth":55,"text":15},{"id":33,"depth":55,"text":34},"conference","9th CVPPA Workshop at ECCV 2024 in Milan","2024-09-29","md","cvppa24-hohl.webp",{},true,"\u002Fde\u002Fresearch\u002F20240929_eccv",{"title":6,"description":41},"de\u002Fresearch\u002F20240929_eccv","EeMAyd37kJFJAuY_6zmytKnCiMO0FRm7kNgU3wMf_ac",{"id":163,"title":164,"authors":165,"body":166,"category":151,"conference":232,"date":233,"description":41,"extension":154,"image":234,"meta":235,"navigation":157,"path":236,"seo":237,"stem":238,"__hash__":239},"research\u002Fde\u002Fresearch\u002F20231002_iccv.md","Towards Automated Regulation of Jacobaea Vulgaris in Grassland using Deep Neural Networks","Moritz Schauer, Renke Hohl, Dennis Vaupel, Diethelm Bienhaus, Seyed Eghbal Ghobadi",{"type":9,"value":167,"toc":228},[168,170,173,180,182,226],[12,169,15],{"id":14},[17,171,172],{},"The highly poisonous ragwort (Jacobaea Vulgaris) is increasingly spreading, posing significant risks to agriculture, livestock, and nature conservation due to the production of toxic pyrrolizidine alkaloids (PAs). The current manual control methods, such as plucking weed, are labor-intensive and time-consuming. This paper introduces a workflow towards automated regulation of J. Vulgaris, which consists of the two independent tasks of deep learning-based monitoring and controlling. We aim to detect and control J. Vulgaris in an early growth stage before the plant can reseed, which challenges the data collection and the training of deep neural networks. Primarily we need to detect the green leaf rosettes on a green meadow. The main focus lies on the monitoring part with synthetic training data generation and a deep neural network-based labeling assistant.",[17,174,22,175],{},[24,176,179],{"href":177,"rel":178,"target":29},"https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023W\u002FCVPPA\u002Fhtml\u002FSchauer_Towards_Automated_Regulation_of_Jacobaea_Vulgaris_in_Grassland_Using_Deep_ICCVW_2023_paper.html",[28],"ICCV 2023 Proceedings",[12,181,34],{"id":33},[36,183,185],{"className":38,"code":184,"language":40,"meta":41,"style":41},"@InProceedings{Schauer_2023_ICCV,\n    author    = {Schauer, Moritz and Hohl, Renke and Vaupel, Dennis and Bienhaus, Diethelm and Ghobadi, Seyed Eghbal},\n    title     = {Towards Automated Regulation of Jacobaea Vulgaris in Grassland Using Deep Neural Networks},\n    booktitle = {Proceedings of the IEEE\u002FCVF International Conference on Computer Vision (ICCV) Workshops},\n    month     = {October},\n    year      = {2023},\n    pages     = {702-711}\n}\n",[43,186,187,192,197,202,207,212,217,222],{"__ignoreMap":41},[46,188,189],{"class":48,"line":49},[46,190,191],{},"@InProceedings{Schauer_2023_ICCV,\n",[46,193,194],{"class":48,"line":55},[46,195,196],{},"    author    = {Schauer, Moritz and Hohl, Renke and Vaupel, Dennis and Bienhaus, Diethelm and Ghobadi, Seyed Eghbal},\n",[46,198,199],{"class":48,"line":61},[46,200,201],{},"    title     = {Towards Automated Regulation of Jacobaea Vulgaris in Grassland Using Deep Neural Networks},\n",[46,203,204],{"class":48,"line":67},[46,205,206],{},"    booktitle = {Proceedings of the IEEE\u002FCVF International Conference on Computer Vision (ICCV) Workshops},\n",[46,208,209],{"class":48,"line":73},[46,210,211],{},"    month     = {October},\n",[46,213,214],{"class":48,"line":79},[46,215,216],{},"    year      = {2023},\n",[46,218,219],{"class":48,"line":85},[46,220,221],{},"    pages     = {702-711}\n",[46,223,224],{"class":48,"line":91},[46,225,142],{},[144,227,146],{},{"title":41,"searchDepth":55,"depth":55,"links":229},[230,231],{"id":14,"depth":55,"text":15},{"id":33,"depth":55,"text":34},"8th CVPPA Workshop at ICCV 2023 in Paris","2023-10-02","cvppa23-schauer.webp",{},"\u002Fde\u002Fresearch\u002F20231002_iccv",{"title":164,"description":41},"de\u002Fresearch\u002F20231002_iccv","iJSAMxb3Eigd2catXoiRE6L4Wt2ZxE-L6AySwP5KVuk",1793017187268]