Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jana Kierdorf"'
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflowe
Externí odkaz:
https://doaj.org/article/b37074ef087b40ee9b6b7663f3dcb163
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-dest
Externí odkaz:
https://doaj.org/article/654541c43f1f4dc1be774b6b20723fe9
Autor:
Jana Kierdorf, Laura Verena Junker‐Frohn, Mike Delaney, Mariele Donoso Olave, Andreas Burkart, Hannah Jaenicke, Onno Muller, Uwe Rascher, Ribana Roscher
Publikováno v:
Journal of field robotics 40(2), 173-192 (2023). doi:10.1002/rob.22122
This article presents GrowliFlower, a georeferenced, image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021. The dataset contains RGB and multispectral orthophotos from which about 1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::859ec0d15c74342d2ed8d643a132cf21
https://doi.org/10.1002/rob.22122
https://doi.org/10.1002/rob.22122
Autor:
Alla Sawatzky, Jana Kierdorf
Although the use of inferential statistical procedures, such as the significance test, is very common, it is usually left open what purpose the use of inferential statistics serves. We show that inferential statistical procedures are not able to prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7de872ab24f34970a3f6b0643db76601
https://doi.org/10.31234/osf.io/5cdu2
https://doi.org/10.31234/osf.io/5cdu2
Publikováno v:
xxAI-Beyond Explainable AI ISBN: 9783031040825
xxAI-Beyond Explainable AI-International Workshop, Held in Conjunction with ICML 2020, Revised and Extended Papers
xxAI-Beyond Explainable AI-International Workshop, Held in Conjunction with ICML 2020, Revised and Extended Papers. Cham: Springer
xxAI-Beyond Explainable AI-International Workshop, Held in Conjunction with ICML 2020, Revised and Extended Papers
xxAI-Beyond Explainable AI-International Workshop, Held in Conjunction with ICML 2020, Revised and Extended Papers. Cham: Springer
Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information ab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::669835b0b0562f7917c48de4840c21a4
https://doi.org/10.1007/978-3-031-04083-2_15
https://doi.org/10.1007/978-3-031-04083-2_15
Autor:
Maurice Günder, Christian Bauckhage, Anne-Katrin Mahlein, Ribana Roscher, Facundo Ramón Ispizua Yamati, Jana Kierdorf
Publikováno v:
GigaScience. 11
UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy archit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3f2ea08e62d53878652e18af91aed8b
https://pubmed.ncbi.nlm.nih.gov/35715875
https://pubmed.ncbi.nlm.nih.gov/35715875
Publikováno v:
Computers and electronics in agriculture 190, 106415-(2021). doi:10.1016/j.compag.2021.106415
Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35eb24761a4d90508993f7c76f47c3a8
https://doi.org/10.1016/j.compag.2021.106415
https://doi.org/10.1016/j.compag.2021.106415