ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-3-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-285-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-285-2024
04 Nov 2024
 | 04 Nov 2024

UAV imaging for spectral characterization of Coffee Leaf Miner (Leucoptera coffeella) infestation in the Cerrado Mineiro region

Vinicius Silva Werneck Orlando, Maria de Lourdes Bueno Trindade Galo, George Deroco Martins, Andrea Maria Lingua, and Vanessa Andaló

Keywords: Aerial Imaging, Spectral Analysis, Sustainable Agriculture, Pest Management, Infestation Monitoring

Abstract. Brazil, the world's largest coffee producer, faces challenges managing the coffee leaf miner (Leucoptera coffeella), a significant pest. This study suggests remote sensing for pest control decisions. Two experimental areas in the Cerrado region of Minas Gerais State were analyzed to spectrally characterize infested plants and estimate the number of mines per plant. Results show the ability to differentiate infested plants with greater reflectance variance in the near infrared at 850nm. The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.