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Rossi, C

Terra, J.A

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Authors

Pravia, V

Terra, J.A

Roel

Rossi, C

Almeida, S.L

Sysskind, M.N

Moreno, L.A

Felipe dos Santos, A

Lacerda, L

Vellidis, G

Pilcon, C

Orlando Costa Barboza, T

Topics

Spatial Variability in Crop, Soil and Natural Resources

Artificial Intelligence (AI) in Agriculture

Type

Oral

Year

2010

2024

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Filter results2 paper(s) found.
1. Does Pasture Longevity Under Direct Grazing Affect Field-scale Sorghum Yield Spatial Variability In Crop-pasture Rotation Systems? Crop yield spatial variability is usually related to terrain attributes and soil properties. In pasture systems, soil properties are affected by animal grazing. However, soil and terrain attributes relation with crop yield variability has not been assessed in crop-pasture rotations.... V. Pravia, J.A. Terra, Roel Download paper
2. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic Parameters The environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States in... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe dos santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando costa barboza Download paper

Filter results2 paper(s) found.

1. Does Pasture Longevity Under Direct Grazing Affect Field-scale Sorghum Yield Spatial Variability In Crop-pasture Rotation Systems?

Crop yield spatial variability is usually related to terrain attributes and soil properties. In pasture systems, soil properties are affected by animal grazing. However, soil and terrain attributes relation with crop yield variability has not been assessed in crop-pasture rotations.... V. Pravia, J.A. Terra, Roel

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2. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic Parameters

The environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States in... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe dos santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando costa barboza

Download paper