Development of a method for detecting the planting and ridge areas in paddy fields using AI, GIS, and precise DEM
In Japan, mowing work on ridges of farms that cultivate rice is difficult for farmers, especially in hilly and mountainous areas. Moreover, geographical information on ridges in paddy fields has not been prepared; such information includes the slope angle, the ridge area, and the ridge rate of the t...
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| Veröffentlicht in: | Precision agriculture 2023-10, Vol.24 (5), p.1862-1888 |
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| creator | Hamano, Mitsuru Shiozawa, Shigeru Yamamoto, Shinya Suzuki, Noritsugu Kitaki, Yuichiro Watanabe, Osamu |
| description | In Japan, mowing work on ridges of farms that cultivate rice is difficult for farmers, especially in hilly and mountainous areas. Moreover, geographical information on ridges in paddy fields has not been prepared; such information includes the slope angle, the ridge area, and the ridge rate of the total paddy field area. This issue causes a level of uncertainty in management analysis in terms of labor costs, including mowing costs, particularly when farmers and agricultural corporations are starting or expanding farm businesses. Therefore, this research developed a method for creating planting area and ridge area polygons in paddy fields to measure the actual areas of both sites using slope angle information and calculating the ridge rates in paddy fields. This study adopts artificial intelligence, geographical information system (GIS), and precision digital elevation model techniques as strategy implementation tools with data prepared by an aerial laser survey of Nagano Prefecture. The model generated using the proposed machine learning tool can automatically detect the planting and ridge areas of paddy fields through aerial images of farmland with more than 96% accuracy. Then, polygons can be created for use in GIS. Furthermore, these polygons can be created for most of the understudied paddy fields, approximately 35 000 hectares throughout Nagano Prefecture, in only 2 to 3 weeks. Therefore, based on these techniques, the slope angles of ridges, the ridge areas, and the ridge rates of paddy areas can be measured through polygons. |
| doi_str_mv | 10.1007/s11119-023-10021-z |
| format | Article |
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Moreover, geographical information on ridges in paddy fields has not been prepared; such information includes the slope angle, the ridge area, and the ridge rate of the total paddy field area. This issue causes a level of uncertainty in management analysis in terms of labor costs, including mowing costs, particularly when farmers and agricultural corporations are starting or expanding farm businesses. Therefore, this research developed a method for creating planting area and ridge area polygons in paddy fields to measure the actual areas of both sites using slope angle information and calculating the ridge rates in paddy fields. This study adopts artificial intelligence, geographical information system (GIS), and precision digital elevation model techniques as strategy implementation tools with data prepared by an aerial laser survey of Nagano Prefecture. The model generated using the proposed machine learning tool can automatically detect the planting and ridge areas of paddy fields through aerial images of farmland with more than 96% accuracy. Then, polygons can be created for use in GIS. Furthermore, these polygons can be created for most of the understudied paddy fields, approximately 35 000 hectares throughout Nagano Prefecture, in only 2 to 3 weeks. Therefore, based on these techniques, the slope angles of ridges, the ridge areas, and the ridge rates of paddy areas can be measured through polygons.</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-023-10021-z</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Agricultural land ; Agriculture ; Artificial intelligence ; Atmospheric Sciences ; Biomedical and Life Sciences ; Chemistry and Earth Sciences ; Computer Science ; Cost analysis ; Cultivation ; Digital Elevation Models ; Farmers ; Farms ; Geographic information systems ; Grain cultivation ; Japan ; labor ; Life Sciences ; Machine learning ; Management analysis ; Mountain regions ; Mountainous areas ; mountains ; Mowing ; paddies ; Physics ; Planting ; Polygons ; precision ; Remote Sensing/Photogrammetry ; rice ; Rice fields ; Ridges ; Soil Science & Conservation ; spatial data ; Statistics for Engineering ; surveys ; uncertainty</subject><ispartof>Precision agriculture, 2023-10, Vol.24 (5), p.1862-1888</ispartof><rights>The Author(s) 2023</rights><rights>The Author(s) 2023. 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Moreover, geographical information on ridges in paddy fields has not been prepared; such information includes the slope angle, the ridge area, and the ridge rate of the total paddy field area. This issue causes a level of uncertainty in management analysis in terms of labor costs, including mowing costs, particularly when farmers and agricultural corporations are starting or expanding farm businesses. Therefore, this research developed a method for creating planting area and ridge area polygons in paddy fields to measure the actual areas of both sites using slope angle information and calculating the ridge rates in paddy fields. This study adopts artificial intelligence, geographical information system (GIS), and precision digital elevation model techniques as strategy implementation tools with data prepared by an aerial laser survey of Nagano Prefecture. The model generated using the proposed machine learning tool can automatically detect the planting and ridge areas of paddy fields through aerial images of farmland with more than 96% accuracy. Then, polygons can be created for use in GIS. Furthermore, these polygons can be created for most of the understudied paddy fields, approximately 35 000 hectares throughout Nagano Prefecture, in only 2 to 3 weeks. 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of a method for detecting the planting and ridge areas in paddy fields using AI, GIS, and precise DEM</title><author>Hamano, Mitsuru ; Shiozawa, Shigeru ; Yamamoto, Shinya ; Suzuki, Noritsugu ; Kitaki, Yuichiro ; Watanabe, Osamu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-3dcf834e0dbe5cd795a05c0d0c3cad39dcf38e9532fe1f7b81f7a77ab1b827183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agricultural land</topic><topic>Agriculture</topic><topic>Artificial intelligence</topic><topic>Atmospheric Sciences</topic><topic>Biomedical and Life Sciences</topic><topic>Chemistry and Earth Sciences</topic><topic>Computer Science</topic><topic>Cost analysis</topic><topic>Cultivation</topic><topic>Digital Elevation Models</topic><topic>Farmers</topic><topic>Farms</topic><topic>Geographic information systems</topic><topic>Grain 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AI, GIS, and precise DEM</atitle><jtitle>Precision agriculture</jtitle><stitle>Precision Agric</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>24</volume><issue>5</issue><spage>1862</spage><epage>1888</epage><pages>1862-1888</pages><issn>1385-2256</issn><eissn>1573-1618</eissn><abstract>In Japan, mowing work on ridges of farms that cultivate rice is difficult for farmers, especially in hilly and mountainous areas. Moreover, geographical information on ridges in paddy fields has not been prepared; such information includes the slope angle, the ridge area, and the ridge rate of the total paddy field area. This issue causes a level of uncertainty in management analysis in terms of labor costs, including mowing costs, particularly when farmers and agricultural corporations are starting or expanding farm businesses. Therefore, this research developed a method for creating planting area and ridge area polygons in paddy fields to measure the actual areas of both sites using slope angle information and calculating the ridge rates in paddy fields. This study adopts artificial intelligence, geographical information system (GIS), and precision digital elevation model techniques as strategy implementation tools with data prepared by an aerial laser survey of Nagano Prefecture. The model generated using the proposed machine learning tool can automatically detect the planting and ridge areas of paddy fields through aerial images of farmland with more than 96% accuracy. Then, polygons can be created for use in GIS. Furthermore, these polygons can be created for most of the understudied paddy fields, approximately 35 000 hectares throughout Nagano Prefecture, in only 2 to 3 weeks. Therefore, based on these techniques, the slope angles of ridges, the ridge areas, and the ridge rates of paddy areas can be measured through polygons.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11119-023-10021-z</doi><tpages>27</tpages><orcidid>https://orcid.org/0009-0006-4854-6127</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural land Agriculture Artificial intelligence Atmospheric Sciences Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Cost analysis Cultivation Digital Elevation Models Farmers Farms Geographic information systems Grain cultivation Japan labor Life Sciences Machine learning Management analysis Mountain regions Mountainous areas mountains Mowing paddies Physics Planting Polygons precision Remote Sensing/Photogrammetry rice Rice fields Ridges Soil Science & Conservation spatial data Statistics for Engineering surveys uncertainty |
| title | Development of a method for detecting the planting and ridge areas in paddy fields using AI, GIS, and precise DEM |
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