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Nabil, M

Mutegi, J

Mohamed, E.S

Mpinganjira, O

Naeve, S.L

Milori, D.M

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Authors

Dias Paiao, G

Nigon, T.J

Fernández, F.G

Cummings, C

Naeve, S.L

Higuti, V.A

Velasquez, A.E

Gasparino, M.V

Magalhães, D.V

Becker, M

Milori, D.M

Aroca, R.V

Amin, M.E

Abdelfattah, M.A

Mohamed, E.S

Belal, A.A

Nabil, M

Mahmoud, A.G

Mpinganjira, O

Topics

Proximal and Remote Sensing

Robotics, Automation, and Small Farm Mechanization

Precision Planting/Harvesting

Plenary Session

Type

Oral

Poster

Year

2020

2022

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Authors

Filter results4 paper(s) found.
1. Estimating greensnap yield damage with canopy reflectance: a case study Grain yield reduction caused by storm-induced plant breakage (green snap) occurs often in corn fields. With climate change and an increasing frequency in the occurrence of extreme weather events, it is essential to develop methods that can accurately estimate green snap damage, so growers can be properly compensated by insurance companies for yield loss. Because plant breakage also affects crop canopy reflectance, this case study aimed to characterize the changes in crop canopy reflectance... G. Dias paiao, T.J. Nigon, F.G. Fernández, C. Cummings, S.L. Naeve
2. LiDAR-based soybean crop segmentation for autonomous navigation The technological advances in the last few decades have greatly changed agricultural operations. In order to became safer, more profitable, efficient, and sustainable, modern farms have adopted the use of sophisticated technologies, such as robots, sensors, aerial images, and GNSS (Global Navigation Satellite System). These technologies not only increase the crop productivity, but also reduce the wide use of water, fertilisers, and pesticides. Due to this, they reduce costs and negative environmental... V.A. Higuti, A.E. Velasquez, M.V. Gasparino, D.V. Magalhães, M. Becker, D.M. Milori, R.V. Aroca Download paper
3. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud
4. Presentation from Otini Mpinganjira ... O. Mpinganjira

Filter results4 paper(s) found.

1. Estimating greensnap yield damage with canopy reflectance: a case study

Grain yield reduction caused by storm-induced plant breakage (green snap) occurs often in corn fields. With climate change and an increasing frequency in the occurrence of extreme weather events, it is essential to develop methods that can accurately estimate green snap damage, so growers can be properly compensated by insurance companies for yield loss. Because plant breakage also affects crop canopy reflectance, this case study aimed to characterize the changes in crop canopy reflectance... G. Dias paiao, T.J. Nigon, F.G. Fernández, C. Cummings, S.L. Naeve

2. LiDAR-based soybean crop segmentation for autonomous navigation

The technological advances in the last few decades have greatly changed agricultural operations. In order to became safer, more profitable, efficient, and sustainable, modern farms have adopted the use of sophisticated technologies, such as robots, sensors, aerial images, and GNSS (Global Navigation Satellite System). These technologies not only increase the crop productivity, but also reduce the wide use of water, fertilisers, and pesticides. Due to this, they reduce costs and negative environmental... V.A. Higuti, A.E. Velasquez, M.V. Gasparino, D.V. Magalhães, M. Becker, D.M. Milori, R.V. Aroca

Download paper

3. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression

Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud

4. Presentation from Otini Mpinganjira

... O. Mpinganjira