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Pereira, M.H

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Authors

Mintesinot, S.M

Santana, C.C

Queiroz, D

Pereira, M.H

Coelho, A.L

Araújo Barbosa, I

Pereira, M.H

Queiroz, D

Coelho, A.L

Sárvio Valente, D

Moreira, M.C

Topics

Remote and Proximal Sensing of Soils and Crops

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Oral

Poster

Year

2026

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Filter results2 paper(s) found.
1. Hyperspectral Estimation of Corn Leaf Chlorophyll Index Using a Wavelet-Guided Spectral Index Optimization Framework The corn leaf chlorophyll index (LCI) is a proxy indicator of plant photosynthetic capacity and nitrogen nutritional status, thereby supporting precision nutrient management and data-driven agronomic decision-making. Conventional methods, such as handheld chlorophyll meters, provide accurate leaf-level measurements but are labor-intensive, time-consuming, and inherently limited in their ability to represent field-scale spatial variability due to their point-based sampling nature. Hyperspectral... S.M. Mintesinot, C.C. Santana, D. Queiroz, M.H. Pereira, A.L. Coelho Download paper
2. Comparison of Orbital and UAV Remote Sensing for Coffee Crop Monitoring in Mountainous Terrain Monitoring coffee crops is an important step for the success of production systems. Recently, manual field inspections have been replaced by automated techniques aimed at improving spatial coverage and reducing costs. One such technique is remote sensing, which can be performed using both orbital platforms and Unmanned Aerial Vehicles (UAVs). However, coffee cultivation presents significant imaging challenges due to plant spacing, where wide row spacing results in greater spectral variability... I. Araújo Barbosa, M.H. Pereira, D. Queiroz, A.L. Coelho, D. Sárvio Valente, M.C. Moreira Download paper

Filter results2 paper(s) found.

1. Hyperspectral Estimation of Corn Leaf Chlorophyll Index Using a Wavelet-Guided Spectral Index Optimization Framework

The corn leaf chlorophyll index (LCI) is a proxy indicator of plant photosynthetic capacity and nitrogen nutritional status, thereby supporting precision nutrient management and data-driven agronomic decision-making. Conventional methods, such as handheld chlorophyll meters, provide accurate leaf-level measurements but are labor-intensive, time-consuming, and inherently limited in their ability to represent field-scale spatial variability due to their point-based sampling nature. Hyperspectral... S.M. Mintesinot, C.C. Santana, D. Queiroz, M.H. Pereira, A.L. Coelho

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2. Comparison of Orbital and UAV Remote Sensing for Coffee Crop Monitoring in Mountainous Terrain

Monitoring coffee crops is an important step for the success of production systems. Recently, manual field inspections have been replaced by automated techniques aimed at improving spatial coverage and reducing costs. One such technique is remote sensing, which can be performed using both orbital platforms and Unmanned Aerial Vehicles (UAVs). However, coffee cultivation presents significant imaging challenges due to plant spacing, where wide row spacing results in greater spectral variability... I. Araújo Barbosa, M.H. Pereira, D. Queiroz, A.L. Coelho, D. Sárvio Valente, M.C. Moreira

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