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| Filter results8 paper(s) found. |
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1. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer VisionThe continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas |
2. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in CornLow altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser |
3. Applying a Bivariate Frequency Ratio Technique for Potato High Yield Susceptibility MappingSpatial variation of soil characteristics and vegetation conditions are viewed as the most important indicators of crop yield status. Therefore, this study was designed to develop a crop yield prediction model through spatial autocorrelation between the actual yield of potato (Solanum tuberosum L.) crop and selected yield status indicators (soil N, EC, pH, texture and vegetation condition), where the vegetation condition was represented by the cumulative normalized difference vegetation index... K. Al-gaadi, A.A. Hassaballa, E. Tola, R. Madugundu, A.G. Kayad |
4. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil DataOur hypothesis was that simple models can be applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models can be used to test different sampling and interpolation approaches commonly applied in precision agriculture and to better predict soil variables at not observed locations. Three strategies for composite sample collection were compared in our study. Point samples were taken 1.) along lines within homogenous... I. Sisák, A. Benő, K. Szabó, M. Kocsis, J. Abonyi |
5. Improving Yield Prediction Accuracy Using Energy Balance Trial, On-the-Go and Remote Sensing ProcedureOur long term experience in the ~23.5 ha research field since 2001 shows that decision support requires complex databases from each management zone within that field (eg. soil physical and chemical parameters, technological, phenological and meteorological data). In the absence of PA sustainable biomass production cannot be achieved. The size of management zones will be ever smaller. Consequently, the on the go and remote sensing data collection should be preferred. The... A. Nyéki , G. Milics, A.J. Kovács, M. Neményi, I. Kulmány, S. Zsebő |
6. Investigating Spatial Relationship of Apparent Electrical Conductivity with Turfgrass and Soil Characteristics in Sand-capped Golf Course FairwaysTurfgrass quality decreases when grown on fine textured soils that are irrigated with poor quality water. As a result, sand-capping (i.e., a sand layer above existing native soil) is now considered during golf course fairway renovation and construction. Mapping spatial variability of soil apparent electrical conductivity (ECa) has recently been suggested to have applications for precision turfgrass management (PTM) in native soil fairways, but sand-capped fairways have received less... C. Straw, B. Wyatt, A.P. Smith, K. Watkins, S. Hong, W. Floyd, D. Williams, C. Garza, T. Jansky |
7. Employment of the SSEB and CROPWAT Models to Estimate the Water Footprint of Potato Grown in Hyper-arid Regions of Saudi ArabiaQuantifying crops’ water footprint (WF) is essential for sustainable agriculture especially in arid regions, which suffers from harsh environmental conditions and severe shortage of freshwater resources such as Saudi Arabia. In this study, WF of irrigated potato crop was estimated for the implementation of precision agriculture techniques. The CROPWAT and the Simplified Surface Energy Balance (SSEB) approaches were adopted. Soil, plant, and yield samples were randomly collected from six... R. Madugundu, K. Al-gaadi, E. Tola |
8. Enabling Field-level Connectivity in Rural Digital Agriculture with Cloud-based LoRaWANThe widespread adoption of next-generation digital agriculture technologies in rural areas faces a critical challenge in the form of inadequate field-level connectivity. Traditional approaches to connecting people fall short in providing cost-effective solutions for many remote agricultural locations, exacerbating the digital divide. Current cellular networks, including 5G with millimeter wave technology, are urban-centric and struggle to meet the evolving digital agricultural needs, presenting... Y. Zhang, J. Bailey, A. Balmos, F.A. Castiblanco rubio, J. Krogmeier, D. Buckmaster, D. Love, J. Zhang, M. Allen |