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| Filter results5 paper(s) found. |
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1. Decision Support System for Precision Agriculture management Case study : El Salihiya –east Nile delta, Egypt.Soil is a complex mixture of living organisms and organic material, along with soil minerals. the main objective of this work is develop a new methods to improve the agricultural management .The current study relies on developing a decision-making model for agricultural operations to manage potato crops in the El Salihiya area using field data,laboratory analysis and field sensor measurements. The precision agriculture decision support system entitled (EGYPADS) was designed and developed... A. Belal , S. abd el-kader, B. Mamdouh , M. A el-shirbeny, M. abdellatif1, M. Jalhoum , M. Zahran, E.S. Mohamed |
2. Monitoring Corn (Zea mays) Yield using Sentinel-2 and Machine Learning for Precision Agriculture ApplicationsCurrently, there is a growing demand to apply precision agriculture (PA) management practices at agricultural fields expecting more efficient and more profitable management. One of PA principal components for site-specific management is crop yield monitoring which varies temporally between seasons and spatially within-field. In this study, we investigated the possibility of monitoring within-field variability of corn grain yield in a 22ha field located in Ferarra, North Italy. Archived yield data... A. Kayad, M. Sozzi, F. Pirotti, F. Marinello, L. Sartori, S. Gatto |
3. Deep Learning is bringing pan-African small holder advisory services based on mid-infrared spectroscopic soil analysis to the next levelThe majority of African smallholder farmers do not have access to soil analytical services. The main reasons are relatively high costs of wet chemical services and difficult logistics. As a result they have to rely on blanket fertilizer recommendations. This often causes poor soil management due to very heterogeneous soil conditions. As a result, the return on investment from blanket fertilizer recommendations is low and fertilizer acceptance is not growing among smallholder farmers. Soil spectral... T. Terhoeven-urselmans, D. Fletcher, M.M. Karanja, J.W. Kamau |
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. Improving Lime and Fertiliser Recommendations for Smallholders Using Co-variate Zoning and Low Cost Mir Soil Testing TechnologySmall-holder farmers lack for them affordable access to crop and field specific lime and fertilisation advice. Another challenge is that while crop and region specific fertiliser blends could be produced, high resolution, unbiased and up to date soil information is lacking and thus crop and region specific blends are not produced. As a result, the farmers are left with a small number of available compound and fertiliser blends that often do not match the crop needs. This is not a convincing situation... T. Terhoeven-urselmans |