Multi-Spectral Imaging for Weed Identification in Herbicides Testing
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Agüera Vega, Francisco; Carvajal Ramírez, Fernando; Martínez Carricondo, Patricio Jesús; Martín Garzón, Gracia Ester; Ortega López, Gloria; [et al.]Date
2022Abstract
new methodology to help to improve the efficiency of herbicide assessment is explained.
It consists of an automatic tool to quantify the percentage of weeds and plants of interest
(sunflowers) that are present in a given area. Images of the crop field taken from Sequoia camera
were used. Firstly, the quality of the images of each band is improved. Later, the resulting multispectral
images are classified into several classes (soil, sunflower and weed) through a novel algorithm
implemented in e-Cognition software. Obtained results of the proposed classifications have
been compared with two deep learning-based segmentation methods (U-Net and FPN).
Palabra/s clave
multi-spectral images
multi-spectral classification
herbicide assessment
deep learning segmentation
e-Cognition