Proceedings
Authors
| Filter results2 paper(s) found. |
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1. Detection of Plants with Xylella fastidiosa in Olive Orchards Using Aerial Multispectral, Thermal Imagery and Machine LearningEarly detection of Xylella fastidiosa in olive orchards remains a significant phytosanitary challenge due to the difficulty of identifying infected plants during the initial symptom development phase. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable approach for disease monitoring at field scale. This study evaluated the potential of spectral indices derived from multispectral and thermal imagery for classifying the occurrence of... F. Silva, M. Antônio, G. Koch, P.A. Moura, C.C. Santana |
2. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal ImageryAccurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana |