Data/hora: 09/06/2024 12:20:02

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	28/08/2023	
Data da última atualização: 	26/10/2023	
Autoria: 	CASTRO, V. H. M. e de; PARREIRAS, T. C.; BOLFE, E. L.	
Afiliação: 	VICTÓRIA HELLENA MATUSEVICIUS E DE CASTRO, UNIVERSIDADE ESTADUAL DE CAMPINAS; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS.	
Título: 	Disease detection in citrus crops using optical and thermal remote sensing: a literature review.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Engenharia na Agricultura, v. 31, p. 140-157, 2023.	
ISSN: 	2175-6813	
DOI: 	https://doi.org/10.13083/reveng.v30i1.15448	
Idioma: 	Português	
Notas: 	Errata - The acknowledgments of the article include: To the State of São Paulo Research Foundation (FAPESP), process number  2019/26222-6. The correct process number is 2022/09319-9.	
Conteúdo: 	Brazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies - most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants.	
Palavras-Chave: 	Agricultura digital; Algoritmos de aprendizado de máquina; Citriculture; Digital agriculture; NDVI.	
Thesagro: 	Citricultura; Doença de Planta; Sensoriamento Remoto.	
Thesaurus Nal: 	Citrus; Greening disease; Plant diseases and disorders; Remote sensing.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/257157/1/AP-Disease-detection-2023.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (21758-1) - UPC - AP - DD -  -  -  - DIS