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| Filter results6 paper(s) found. |
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1. Mapping African soils at 30m resolution - iSDAsoil: leveraging spatial agronomy in farm-level advisory for smallholdersField level soil data has been the foundation of agronomic advisory, but traditional methods involving on-farm sampling are too expensive for a large proportion of African smallholders. Building on the work of the African Soil Information Service (AfSIS), Innovative Solutions for Decision Agriculture (iSDA) and partners have created an agronomic soil database which covers the entire African continent at a spatial resolution of 30 m. “iSDAsoil” combines remote sensing data and other... J. Crouch |
2. CropSAT – opportunities for applications in precision agriculture in AfricaThe present paper aims at describing the CropSAT system, a Sentinel-2-based interactive decision support system (DSS) that provides vegetation index (VI) maps free-of-charge all across the globe for different applications in precision agriculture. We summarize research results from the ongoing developmental process and pointing to opportunities for development and application in precision agriculture in Africa. The DSS was initially developed in a research project at the Swedish University of... O. Alshihabi, I. Nouiri, M. Mechri, H. Angar, K. Piikki, J. Martinsson, M. Söderström |
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 |
4. 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 |
5. Modelling Fertigation and Micro-Climate Parameters for Greenhouse Tomato (Solanum Lycopersicum L.)Amidst the hiking price of fertilizer and projected water scarcity across the world, it is imperative to explore the interaction between fertilizer, irrigation and genotype notwithstanding the micro-climate parameters so as to maximize yield while protecting the environment. The Decision Support System for Agrotechnology Transfer (DSSAT) is a model which employs all these input factors to help predict yield and thereby make an informed decision. The study sort to calibrate and validate the... Y.K. Agbemabiese, P. Abubakari, P.K. Dzomeku, I. Shaibu |
6. High-Throughput Field Phenotyping of Ascochyta Blight Disease Severity in Chickpea Using Multispectral ImagingAscochyta blight (AB) caused by Ascochyta rabiei (Pass.) Labr. is an important and widespread disease of chickpea (Cicer arietinum L.) worldwide. The disease is particularly severe under cool and humid weather conditions, leading to crop losses at all stages of chickpea growth. Screening for resistant cultivars remains the most effective, economical and ecological method of disease management. However, traditional phenotyping methods that relying on trained experts are... F. Ibn el mokhtar, S. Krimibencheqroun , , A. Harkani , H. Houmairi , O. Idrissi , E. Abdellah , E. Abdellah |