Monitoring Plant Status and Fertilization Strategy through Multispectral Images
A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluatio...
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/2/435 |
| _version_ | 1798026120989245440 |
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| author | Matheus Cardim Ferreira Lima Anne Krus Constantino Valero Antonio Barrientos Jaime del Cerro Juan Jesús Roldán-Gómez |
| author_facet | Matheus Cardim Ferreira Lima Anne Krus Constantino Valero Antonio Barrientos Jaime del Cerro Juan Jesús Roldán-Gómez |
| author_sort | Matheus Cardim Ferreira Lima |
| collection | DOAJ |
| description | A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform. |
| first_indexed | 2024-04-11T18:30:01Z |
| format | Article |
| id | doaj.art-a8ad6cfd8e764bb88b19ae7ab4803c13 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:30:01Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a8ad6cfd8e764bb88b19ae7ab4803c132022-12-22T04:09:28ZengMDPI AGSensors1424-82202020-01-0120243510.3390/s20020435s20020435Monitoring Plant Status and Fertilization Strategy through Multispectral ImagesMatheus Cardim Ferreira Lima0Anne Krus1Constantino Valero2Antonio Barrientos3Jaime del Cerro4Juan Jesús Roldán-Gómez5Department of Agroforest Ecosystems, ETSI Agrónomos, Universidad Politécnica de Valencia, 46022 Valencia, SpainDepartment of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartment of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, SpainCentre for Automation and Robotics (CSIC-UPM), 28006 Madrid, SpainCentre for Automation and Robotics (CSIC-UPM), 28006 Madrid, SpainCentre for Automation and Robotics (CSIC-UPM), 28006 Madrid, SpainA crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.https://www.mdpi.com/1424-8220/20/2/435multispectral imagecomputer visionprecision agriculturevegetation indicesmorphological features |
| spellingShingle | Matheus Cardim Ferreira Lima Anne Krus Constantino Valero Antonio Barrientos Jaime del Cerro Juan Jesús Roldán-Gómez Monitoring Plant Status and Fertilization Strategy through Multispectral Images Sensors multispectral image computer vision precision agriculture vegetation indices morphological features |
| title | Monitoring Plant Status and Fertilization Strategy through Multispectral Images |
| title_full | Monitoring Plant Status and Fertilization Strategy through Multispectral Images |
| title_fullStr | Monitoring Plant Status and Fertilization Strategy through Multispectral Images |
| title_full_unstemmed | Monitoring Plant Status and Fertilization Strategy through Multispectral Images |
| title_short | Monitoring Plant Status and Fertilization Strategy through Multispectral Images |
| title_sort | monitoring plant status and fertilization strategy through multispectral images |
| topic | multispectral image computer vision precision agriculture vegetation indices morphological features |
| url | https://www.mdpi.com/1424-8220/20/2/435 |
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