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| Filter results4 paper(s) found. |
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1. 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 |
2. Mapping African soils at 30m resolution - iSDAsoil - Eastern 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... C. Van beek, M. Chernet, S. Aston, M. Miller, J. Collinson, K. Shephard, J. Crouch, T. Terhoeven-urselmans |
3. Monitoring Grazing Goats' Behavior Using Sensors and Satellite Remote SensingThe recent development of new methods for remotely observing animal behavior using electronic sensors such as global positioning systems (GPS) and three-axis accelerometers to monitor and record behavior at different spatial and temporal scales presents real opportunities for better understanding and interpreting the behaviors of grazing animals. The goal of this study was to distinguish different behavioral categories of grazing goats by combining GPS collars, accelerometers, and satellite remote... Y. Chebli, S. El otmani, J. Cabaraux, M. Chentouf , A. Keli, F. Elame |
4. Development and Validation of an Algorithm for Optical Sensor-based Nitrogen Fertilization for Durum Wheat in Central Ethiopia I. Algorithm DevelopmentMonitoring in-season nitrogen (N) management using proximal optical sensors is often considered highly cost-effective and efficient. Developing a successful sensor-based algorithm to optimize in-season N application for durum wheat could enhance productivity and nitrogen use efficiency. This study aimed to develop and validate a sensor-based algorithm for N fertilization of durum wheat in central Ethiopia. The developed algorithm is based on the prediction of total N uptake from normalized difference... S. Endris, T. Mesfin, O. Atnafu, S. Tamiru |