AI/ML

Monitoring Plant Status and Fertilization Strategy through Multispectral Images.




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Monitoring Plant Status and Fertilization Strategy through Multispectral Images.

Sensors (Basel). 2020 Jan 13;20(2):

Authors: Cardim Ferreira Lima M, Krus A, Valero C, Barrientos A, Del Cerro J, Roldán-Gómez JJ

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.

PMID: 31941027 [PubMed - indexed for MEDLINE]

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