Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success
The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/5/3/79 |
| _version_ | 1797519613032923136 |
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| author | Lorena Parra David Mostaza-Colado Salima Yousfi Jose F. Marin Pedro V. Mauri Jaime Lloret |
| author_facet | Lorena Parra David Mostaza-Colado Salima Yousfi Jose F. Marin Pedro V. Mauri Jaime Lloret |
| author_sort | Lorena Parra |
| collection | DOAJ |
| description | The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture’s size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species. |
| first_indexed | 2024-03-10T07:45:16Z |
| format | Article |
| id | doaj.art-361a310c5ff5458fbd7ce7ff5e879686 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T07:45:16Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-361a310c5ff5458fbd7ce7ff5e8796862023-11-22T12:43:05ZengMDPI AGDrones2504-446X2021-08-01537910.3390/drones5030079Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment SuccessLorena Parra0David Mostaza-Colado1Salima Yousfi2Jose F. Marin3Pedro V. Mauri4Jaime Lloret5Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Calle Paranimf 1, Grau de Gandia, 46730 Valencia, SpainInstituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), Finca “El Encin”, A-2, Km 38, 2, 28805 Alcalá de Henares, SpainInstituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), Finca “El Encin”, A-2, Km 38, 2, 28805 Alcalá de Henares, SpainAreaverde MG Projects SL. C/Oña, 43, 28933 Madrid, SpainInstituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), Finca “El Encin”, A-2, Km 38, 2, 28805 Alcalá de Henares, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Calle Paranimf 1, Grau de Gandia, 46730 Valencia, SpainThe use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture’s size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.https://www.mdpi.com/2504-446X/5/3/79chickpealentilvegetation indexartificial neural networkaggregation |
| spellingShingle | Lorena Parra David Mostaza-Colado Salima Yousfi Jose F. Marin Pedro V. Mauri Jaime Lloret Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success Drones chickpea lentil vegetation index artificial neural network aggregation |
| title | Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success |
| title_full | Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success |
| title_fullStr | Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success |
| title_full_unstemmed | Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success |
| title_short | Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success |
| title_sort | drone rgb images as a reliable information source to determine legumes establishment success |
| topic | chickpea lentil vegetation index artificial neural network aggregation |
| url | https://www.mdpi.com/2504-446X/5/3/79 |
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