Hyperspectral Characterization of Coffee Leaf Miner ( Leucoptera coffeella ) (Lepidoptera: Lyonetiidae) Infestation Levels: A Detailed Analysis

Vinicius Silva Werneck Orlando (), Maria de Lourdes Bueno Trindade Galo, George Deroco Martins, Andrea Maria Lingua, Gleice Aparecida de Assis and Elena Belcore
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Vinicius Silva Werneck Orlando: Postgraduate Program in Cartographic Sciences, São Paulo State University, Presidente Prudente 19060-900, SP, Brazil
Maria de Lourdes Bueno Trindade Galo: Postgraduate Program in Cartographic Sciences, São Paulo State University, Presidente Prudente 19060-900, SP, Brazil
George Deroco Martins: Institute of Geography, Federal University of Uberlândia, Monte Carmelo 38500-000, MG, Brazil
Andrea Maria Lingua: Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, 10129 Torino, Italy
Gleice Aparecida de Assis: Institute of Agricultural Sciences, Federal University of Uberlândia, Monte Carmelo 38500-000, MG, Brazil
Elena Belcore: Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, 10129 Torino, Italy

Agriculture, 2024, vol. 14, issue 12, 1-12

Abstract: Brazil is the largest coffee producer in the world. However, it has been a challenge to manage the main pest affecting the plant’s foliar part, the Coffee Leaf Miner (CLM) Leucoptera coffeella (Lepidoptera: Lyonetiidae). To mitigate this, remote sensing has been employed to spectrally characterize various stresses on coffee trees. This study establishes the groundwork for efficient pest detection by investigating the spectral characteristics of CLM infestation at different levels. This research aims to characterize the spectral signature of leaves at different CLM levels of infestation and identify the optimal spectral regions for discriminating these levels. To achieve this, hyperspectral reflectance measurements were made of healthy and infested leaves, and the classes of infested leaves were grouped into minimally, moderately, and severely infested. As the infestation level rises, the 700 nm region becomes increasingly suitable for distinguishing between infestation levels, with the visible region also proving significant, particularly during severe infestations. Reflectance thresholds established in this study provide a foundation for agronomic references related to CLM. These findings lay the essential groundwork for enhancing monitoring and early detection systems and underscore the value of terrestrial hyperspectral data for developing sustainable pest management strategies in coffee crops.

Keywords: spectral signature; precision agriculture; pest management (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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