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UID:pretalx-foss4g-asia-2023-RNCAQ8@talks.osgeo.org
DTSTART;TZID=KST:20231130T151000
DTEND;TZID=KST:20231130T153000
DESCRIPTION:The quality of wine grapes is heavily influenced by environment
 al conditions such as geology\, meteorology\, geomorphology\, and more. Th
 erefore\, it's necessary to consider these factors when selecting the loca
 tion of a vineyard and the specific variety of wine grapes to be cultivate
 d. However\, these selections are often based on the experience of the far
 mers\, making it a barrier for newcomers and raising concerns about potent
 ial economic losses from inappropriate choices of vineyard locations and v
 arieties. In this research\, we attempt to estimate suitable areas for win
 e grape cultivation using machine learning\, utilizing the distribution of
  vineyards as supervised data. The evaluation areas include the surroundin
 gs of Ueda\, Toumi\, and Saku City in Nagano Prefecture. The locations of 
 vineyards were identified through field surveys and interview surveys\, an
 d the data were aggregated on a 3rd level Japanese Standard Grid. The envi
 ronmental factors used for evaluation include geology\, slope angle\, slop
 e direction\, average annual temperature\, lowest annual temperature\, and
  highest annual temperature. These factors are publicly available in Data 
 PNG format\, and a Python script was created to aggregate these data at th
 e 3rd Standard Grid. By incorporating the spatial distribution of existing
  vineyards as the supervised data and analyzing various environmental fact
 ors\, this approach aims to provide a more systematic and accessible way t
 o identify optimal vineyard locations\, thereby reducing reliance on indiv
 idual farmer experience.
DTSTAMP:20260609T150315Z
LOCATION:Seoul Archive
SUMMARY:Estimation of Vineyard Suitable Areas Using Machine Learning and Da
 ta PNG - Nobusuke Iwasaki
URL:https://talks.osgeo.org/foss4g-asia-2023/talk/RNCAQ8/
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