Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.

The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the ?Raster Calculator? obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones.

Bibliographic Details

Main Authors:	SANTOS, L. M. dos, FERRAZ, G. A. e S., MARIN, D. B., CARVALHO, M. A. de F., DIAS, J. E. L., ALECRIM, A. de O., SILVA, M. de L. O. e
Other Authors:	LUANA MENDES DOS SANTOS, UFLA; GABRIEL ARAÚJO E SILVA FERRAZ, UFLA; DIEGO BEDIN MARIN, UFLA; MILENE ALVES DE FIGUEIREDO CARVALHO, CNPCa; JESSICA ELLEN LIMA DIAS, HUNGARIAN UNIVERSITY OF AGRICULTURE AND LIFE SCIENCES; ADEMILSON DE OLIVEIRA ALECRIM, UFLA; MIRIAN DE LOURDES OLIVEIRA E SILVA, UFLA.
Format:	Artigo de periódico biblioteca
Language:	Ingles English
Published:	2022-05-23
Subjects:	Agricultura digital, Agricultura de Precisão, Sensoriamento Remoto, Coffea Arábica, Precision agriculture, Remote sensing, Unmanned aerial vehicles,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143380
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