Monitoring Plant Status and Fertilization Strategy through Multispectral Images

Ferreira Lima, Matheus Cardim and Krus, Anne and Valero Ubierna, Constantino and Barrientos Cruz, Antonio and Cerro Giner, Jaime Del and Roldán-Gómez, Juan Jesús (2020). Monitoring Plant Status and Fertilization Strategy through Multispectral Images. "Sensors", v. 20 (n. 2); p. 435. ISSN 1424-8220. https://doi.org/10.3390/s20020435.

Description

Title: Monitoring Plant Status and Fertilization Strategy through Multispectral Images
Author/s:
  • Ferreira Lima, Matheus Cardim
  • Krus, Anne
  • Valero Ubierna, Constantino
  • Barrientos Cruz, Antonio
  • Cerro Giner, Jaime Del
  • Roldán-Gómez, Juan Jesús
Item Type: Article
Título de Revista/Publicación: Sensors
Date: January 2020
ISSN: 1424-8220
Volume: 20
Subjects:
Freetext Keywords: multispectral image; computer vision; precision agriculture; vegetation indices; morphological features
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Ingeniería Agroforestal
UPM's Research Group: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA; Robótica y Cibernética RobCib
Creative Commons Licenses: None

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Abstract

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

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC1920280080SUREVEGConstantino Valero UbiernaStrip-cropping and recycling of waste for biodiverse and resource-Efficient intensive VEGetable production.

More information

Item ID: 66810
DC Identifier: https://oa.upm.es/66810/
OAI Identifier: oai:oa.upm.es:66810
DOI: 10.3390/s20020435
Official URL: https://doi.org/10.3390/s20020435
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 21 Apr 2021 13:32
Last Modified: 21 Apr 2021 13:32
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