Disease detection in citrus crops using optical and thermal remote sensing: a literature review.

• Autor(a) principal: CASTRO, V. H. M. e de
• Data de Publicação: 2023
• Outros Autores: PARREIRAS, T. C., BOLFE, E. L.
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1156159; https://doi.org/10.13083/reveng.v30i1.15448
• topic: Agricultura digital; NDVI; Algoritmos de aprendizado de máquina; Digital agriculture; Citriculture; Citricultura; Doença de Planta; Sensoriamento Remoto; Citrus; Plant diseases and disorders; Greening disease; Remote sensing

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.