Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification
An unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/10/9/416 |
| _version_ | 1797553127665172480 |
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| author | Pei-Chun Chen Yen-Cheng Chiang Pei-Yi Weng |
| author_facet | Pei-Chun Chen Yen-Cheng Chiang Pei-Yi Weng |
| author_sort | Pei-Chun Chen |
| collection | DOAJ |
| description | An unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships in the Chianan Plain of Chiayi County, Taiwan. About 100 ha of farmland in each township was selected as a sample area, and a quadcopter and a handheld fixed-wing drone were used to capture visible-light images and multispectral images. The survey was carried out from August to October 2018 and aerial photographs were captured in clear and dry weather. This study used high-resolution images captured from a UAV to classify the uses of agricultural land, and then employed information from multispectral images and elevation data from a digital surface model. The results revealed that visible-light images led to low interpretation accuracy. However, multispectral images and elevation data increased the accuracy rate to nearly 90%. Accordingly, such images and data can effectively enhance the accuracy of land use classification. The technology can reduce costs that are associated with labor and time and can facilitate the establishment of a real-time mapping database. |
| first_indexed | 2024-03-10T16:11:00Z |
| format | Article |
| id | doaj.art-c819b4e4fb824bac96662528ecd31d0f |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T16:11:00Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-c819b4e4fb824bac96662528ecd31d0f2023-11-20T14:29:49ZengMDPI AGAgriculture2077-04722020-09-0110941610.3390/agriculture10090416Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use ClassificationPei-Chun Chen0Yen-Cheng Chiang1Pei-Yi Weng2Department of Landscape Architecture, National Chiayi University, Chiayi 60004, TaiwanDepartment of Landscape Architecture, National Chiayi University, Chiayi 60004, TaiwanDepartment of Plant Industry, National Pingtung University of Science and Technology, Pingtung 91201, TaiwanAn unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships in the Chianan Plain of Chiayi County, Taiwan. About 100 ha of farmland in each township was selected as a sample area, and a quadcopter and a handheld fixed-wing drone were used to capture visible-light images and multispectral images. The survey was carried out from August to October 2018 and aerial photographs were captured in clear and dry weather. This study used high-resolution images captured from a UAV to classify the uses of agricultural land, and then employed information from multispectral images and elevation data from a digital surface model. The results revealed that visible-light images led to low interpretation accuracy. However, multispectral images and elevation data increased the accuracy rate to nearly 90%. Accordingly, such images and data can effectively enhance the accuracy of land use classification. The technology can reduce costs that are associated with labor and time and can facilitate the establishment of a real-time mapping database.https://www.mdpi.com/2077-0472/10/9/416unmanned aerial vehicleagricultural surveyland use |
| spellingShingle | Pei-Chun Chen Yen-Cheng Chiang Pei-Yi Weng Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification Agriculture unmanned aerial vehicle agricultural survey land use |
| title | Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification |
| title_full | Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification |
| title_fullStr | Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification |
| title_full_unstemmed | Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification |
| title_short | Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification |
| title_sort | imaging using unmanned aerial vehicles for agriculture land use classification |
| topic | unmanned aerial vehicle agricultural survey land use |
| url | https://www.mdpi.com/2077-0472/10/9/416 |
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