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Oliveira, M.D

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Authors

Zavala, E

, G

Oliveira, M.D

Khalid, H

, P

Alves de Morais, R.M

, G

Faria, R.D

Silva, L.S

Oliveira, M.D

Zavala, E.H

Baker, A.P

, G

Oliveira, M.D

Zavala, E.H

Alves de Morais, R.M

Amaral, M.M

de Castro, A.�

Silva, L.S

Topics

Remote and Proximal Sensing of Soils and Crops

Agricultural Robotics, Automation, and Mechanization

Type

Poster

Year

2026

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Authors

Filter results3 paper(s) found.
1. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor Analysis The assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evaluate... E. Zavala, G. , M.D. Oliveira, H. Khalid, P.
2. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot. The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availability... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala
3. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral Camera Selective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with hyperspectral... A. Palma diniz baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A.

Filter results3 paper(s) found.

1. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor Analysis

The assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evaluate... E. Zavala, G. , M.D. Oliveira, H. Khalid, P.

2. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot.

The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availability... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala

3. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral Camera

Selective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with hyperspectral... A. Palma diniz baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A.