Monitoring Plant Status and Fertilization Strategy through Multispectral Images
by
Matheus Cardim Ferreira Lima 1,2,*
, Anne Krus 3, Constantino Valero 3, Antonio Barrientos 4, Jaime del Cerro 4 and Juan Jesús Roldán-Gómez 4,5
Matheus Cardim Ferreira Lima 1,2,*, Anne Krus 3, Constantino Valero 3, Antonio Barrientos 4, Jaime del Cerro 4 and Juan Jesús Roldán-Gómez 4,5
1
Department of Agroforest Ecosystems, ETSI Agrónomos, Universidad Politécnica de Valencia, 46022 Valencia, Spain
2
Research and Extension Unit (AGDR), Food and Agriculture Organization of the United Nations (FAO), 00153 Rome, Italy
3
Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
4
Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain
5
Department of Computer Engineering, Higher Polytechnic School, Autonomous University of Madrid (UAM), 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 435; https://doi.org/10.3390/s20020435
Received: 17 December 2019 / Revised: 9 January 2020 / Accepted: 9 January 2020 / Published: 13 January 2020
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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.
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Keywords:
multispectral image; computer vision; precision agriculture; vegetation indices; morphological features
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MDPI and ACS Style
Cardim Ferreira Lima, M.; Krus, A.; Valero, C.; Barrientos, A.; del Cerro, J.; Roldán-Gómez, J.J. Monitoring Plant Status and Fertilization Strategy through Multispectral Images. Sensors 2020, 20, 435.
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