Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery
Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, diff...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Drones |
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| Online Access: | http://www.mdpi.com/2504-446X/2/3/22 |
| _version_ | 1818854588276539392 |
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| author | Ola Hall Sigrun Dahlin Håkan Marstorp Maria Francisca Archila Bustos Ingrid Öborn Magnus Jirström |
| author_facet | Ola Hall Sigrun Dahlin Håkan Marstorp Maria Francisca Archila Bustos Ingrid Öborn Magnus Jirström |
| author_sort | Ola Hall |
| collection | DOAJ |
| description | Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems. |
| first_indexed | 2024-12-19T07:55:06Z |
| format | Article |
| id | doaj.art-a4407e4d37cc4f02a57e3370ee1e1052 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-12-19T07:55:06Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-a4407e4d37cc4f02a57e3370ee1e10522022-12-21T20:30:02ZengMDPI AGDrones2504-446X2018-06-01232210.3390/drones2030022drones2030022Classification of Maize in Complex Smallholder Farming Systems Using UAV ImageryOla Hall0Sigrun Dahlin1Håkan Marstorp2Maria Francisca Archila Bustos3Ingrid Öborn4Magnus Jirström5Department of Human Geography, Lund University, 223 00 Lund, SwedenDepartment of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, SwedenDepartment of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, SwedenDepartment of Human Geography, Lund University, 223 00 Lund, SwedenDepartment of Crop Production Ecology, Swedish University of Agricultural Sciences; 750 07 Uppsala, SwedenDepartment of Human Geography, Lund University, 223 00 Lund, SwedenYield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems.http://www.mdpi.com/2504-446X/2/3/22UAVremote sensingmaizeOBIAGhana |
| spellingShingle | Ola Hall Sigrun Dahlin Håkan Marstorp Maria Francisca Archila Bustos Ingrid Öborn Magnus Jirström Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery Drones UAV remote sensing maize OBIA Ghana |
| title | Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery |
| title_full | Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery |
| title_fullStr | Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery |
| title_full_unstemmed | Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery |
| title_short | Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery |
| title_sort | classification of maize in complex smallholder farming systems using uav imagery |
| topic | UAV remote sensing maize OBIA Ghana |
| url | http://www.mdpi.com/2504-446X/2/3/22 |
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