[{"data":1,"prerenderedAt":206},["ShallowReactive",2],{"content-en-\u002Fen\u002Fhistory":3},{"id":4,"title":5,"body":6,"description":198,"extension":199,"meta":200,"navigation":201,"path":202,"seo":203,"stem":204,"__hash__":205},"content\u002Fen\u002Fhistory.md","Project Background: The Origins of aimGrassland",{"type":7,"value":8,"toc":189},"minimark",[9,13,34,39,58,63,81,85,95,115,119,138,145,149,169,173],[10,11,5],"h1",{"id":12},"project-background-the-origins-of-aimgrassland",[14,15,16,17,21,22,25,26,29,30,33],"p",{},"aimGrassland (\"",[18,19,20],"strong",{},"Artificial Intelligence for Maintenance of Grassland","\") builds upon two projects that complemented each other perfectly: the EIP-Agri project ",[18,23,24],{},"Antago Senecio"," and the collaborative research project ",[18,27,28],{},"KIhUG",". By combining the insights from both initiatives, we have developed an approach to support future grassland management in a ",[18,31,32],{},"data-driven, selective, and chemical-free manner",".",[35,36,38],"h2",{"id":37},"starting-point-1-antago-senecio-integrated-strategies-against-ragwort","Starting Point 1: Antago Senecio – Integrated Strategies Against Ragwort",[14,40,41,42,44,45,49,50,53,54,57],{},"The ",[18,43,24],{}," project focused on a central challenge: Ragwort (",[46,47,48],"em",{},"Jacobaea vulgaris",", syn.: ",[46,51,52],{},"Senecio jacobaea",") frequently occurs on extensively managed grassland and can compromise its use as fodder due to toxic pyrrolizidine alkaloids. The goal was to develop ",[18,55,56],{},"nature-friendly and integrated strategies"," to regulate ragwort while preserving species-rich meadows.",[14,59,60],{},[18,61,62],{},"Key Learnings from Antago Senecio",[64,65,66,74],"ul",{},[67,68,69,70,73],"li",{},"In practice, ",[18,71,72],{},"reliable monitoring"," is essential to detect the occurrence and dynamics of problematic plants at an early stage.",[67,75,76,77,80],{},"Grassland management often lacks ",[18,78,79],{},"selective, efficient tools"," that preserve accompanying vegetation and reduce conflicting objectives (e.g., nature conservation requirements vs. cutting times).",[35,82,84],{"id":83},"starting-point-2-kihug-ai-based-detection-as-a-technological-foundation","Starting Point 2: KIhUG – AI-Based Detection as a Technological Foundation",[14,86,87,88,90,91,94],{},"To address the need for digital tools, the ",[18,89,28],{}," project was established as a collaboration focusing on the ",[18,92,93],{},"AI-supported detection of weeds",". The research focused on:",[64,96,97,103,109],{},[67,98,41,99,102],{},[18,100,101],{},"generation of training data (including synthetic data)",",",[67,104,41,105,108],{},[18,106,107],{},"training of artificial neural networks"," for reliable detection,",[67,110,111,114],{},[18,112,113],{},"System integration and robotics"," concepts to derive targeted control measures from detection data.",[35,116,118],{"id":117},"why-a-new-project-was-necessary-from-detecting-and-understanding-to-applying","Why a New Project Was Necessary: From \"Detecting\" and \"Understanding\" to \"Applying\"",[14,120,121,122,125,126,129,130,133,134,137],{},"Antago Senecio primarily provided the ",[18,123,124],{},"ecological, biological, and agronomic"," foundations, as well as a strong network of practitioners. KIhUG created the ",[18,127,128],{},"technological basis"," for automatically detecting problem plants. However, it became clear that a practical solution requires both: ",[18,131,132],{},"ecological system understanding"," and ",[18,135,136],{},"robust digital technology"," that functions reliably in the field.",[14,139,140,141,144],{},"This is precisely where aimGrassland steps in: We combine the experience from both projects to further develop monitoring and decision-making in grassland management in a ",[18,142,143],{},"site-adapted, efficient, and environmentally friendly"," way (integrating sensors\u002Fdrones, AI analysis, and future control tools).",[35,146,148],{"id":147},"what-aimgrassland-adopts-from-its-predecessors","What aimGrassland Adopts from its Predecessors",[64,150,151,157,163],{},[67,152,153,156],{},[18,154,155],{},"Practical Relevance:"," Research questions and field tests are directly oriented toward the real-world challenges of grassland farms.",[67,158,159,162],{},[18,160,161],{},"Evidence-Based Foundations:"," Management approaches are rigorously evaluated for ecological compatibility and effectiveness.",[67,164,165,168],{},[18,166,167],{},"Digital Feasibility:"," AI models, data collection, and system logic are designed for practical application (e.g., generating maps\u002Foverviews as a basis for decision-making).",[35,170,172],{"id":171},"further-links","Further Links",[64,174,175,183],{},[67,176,177],{},[178,179,182],"a",{"href":180,"target":181},"https:\u002F\u002Fantagosenecio.wordpress.com","_blank","Antago Senecio – Project Page",[67,184,185],{},[178,186,188],{"href":187,"target":181},"https:\u002F\u002Finstitut-f-r-technik-und-informatik.git-pages.thm.de\u002Fprojects\u002Fkihug\u002Fdocs\u002F","KIhUG – Project Page",{"title":190,"searchDepth":191,"depth":191,"links":192},"",2,[193,194,195,196,197],{"id":37,"depth":191,"text":38},{"id":83,"depth":191,"text":84},{"id":117,"depth":191,"text":118},{"id":147,"depth":191,"text":148},{"id":171,"depth":191,"text":172},"aimGrassland (\"Artificial Intelligence for Maintenance of Grassland\") builds upon two projects that complemented each other perfectly: the EIP-Agri project Antago Senecio and the collaborative research project KIhUG. By combining the insights from both initiatives, we have developed an approach to support future grassland management in a data-driven, selective, and chemical-free manner.","md",{},true,"\u002Fen\u002Fhistory",{"title":5,"description":198},"en\u002Fhistory","vfAbtQDO3io6mOoZEYwdT4QAVwvRgoVP51bwo4pD0tE",1793017186226]