Multi-Spectral Imaging for Weed Identification in Herbicides Testing

Autor:	Luis O. López, Gloria Ortega, Francisco Agüera-Vega, Fernando Carvajal-Ramírez, Patricio Martínez-Carricondo, Ester M. Garzón
Rok vydání:	2022
Předmět:	Applied Mathematics General Medicine Information Systems
Zdroj:	Informatica. :771-793
ISSN:	1822-8844 0868-4952
Popis:	A 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 multi-spectral 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).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::08ad0738ee120bd0a0f0e7d5b9ea5d93 https://doi.org/10.15388/22-infor498 Zobrazit plný text záznamu