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AfPCA Proceedings 2024

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Kioko, W
Singh, P
Sogbedji, J
Gasparino, M.V
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Authors
Kioko, W
Kioko, W
Kioko, W
Kioko, W
Kitonyo, O
Chemiming'wa, G
Gebeyehu, S
Murori, R
Higuti, V.A
Velasquez, A.E
Gasparino, M.V
Magalhães, D.V
Becker, M
Milori, D.M
Aroca, R.V
Crouch, J
Shephard, K
Miller, M
Collinson, J
Singh, P
Pypers, P
van den Bosch, R
van Beek, C
Chernet, M
Aston, S
Topics
Precision Agriculture for Field and Plantation Crops
Robotics, Automation, and Small Farm Mechanization
Plenary
Type
Oral
Year
2020
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Authors

Filter results3 paper(s) found.

1. Effect of Plant Population and Nitrogen Rate on Growth and Yield of Two Pre-release rice varieties in Kenya

The application of precision agriculture (PA) through the optimization of plant population and management of fertilizer nitrogen (N) can increase rice yield. Current rice (Oryza sativa) plant population and fertilizer nitrogen (N) management guidelines in  Kenya were developed from research conducted about three decades. These management guidelines might still be robust and could be applied to recently released as well as close-to- release varieties. However, the yield potential of... W. Kioko, W. Kioko, W. Kioko, W. Kioko, O. Kitonyo, G. Chemiming'wa, S. Gebeyehu, R. Murori

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

3. Mapping African soils at 30m resolution - iSDAsoil - Western Time Zones

“iSDAsoil” combines remote sensing data and other geospatial information with carefully stratified point samples subjected to spectral analysis and traditional wet chemistry reference analysis. State of the art machine learning techniques were used to create digital maps of 17 agronomically important soil properties at 3 depths, including estimates of uncertainty. iSDAsoil is designed to encourage sharing and we hope that the owners of other soil and agronomic data, in industry... J. Crouch, K. Shephard, M. Miller, J. Collinson, P. Singh, P. Pypers, R. Van den bosch, C. Van beek, M. Chernet, S. Aston