Authors
| Filter results12 paper(s) found. |
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1. Estimation of Rice Yield from MODIS Data in West Java, IndonesiaChiharu Hongo1*, Takaaki Furukawa1, Gunardi Sigit2, Masayasu Maki3, Koki Honma3,... C. Hongo, T. Furukawa, G. Sigit, M. Maki, K. Honma, K. Yoshida, K. Oki, H. Shirakawa |
2. FOODIE Data Model for Precision AgricultureThe agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production. The FOODIE service platform deals with including their thematic, spatial, and temporal... K. Charvat, T. Reznik, K. Charvat jr., V. Lukas, S. Horakova, M. Kepka |
3. Quo Vadis Precision FarmingThe agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenced... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka |
4. Map Whiteboard As Collaboration Tool for Smart Farming Advisory ServicesPrecision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr. |
5. 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 |
6. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
7. Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation ManagementThe Western United States is currently experiencing a “Mega Drought”. This makes efficient water use more important than ever. Turfgrass is a major vegetation type in urban areas and performs many ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere and reducing wild-fire risk. There are now more acres of irrigated turfgrass (>40 million) in the USA than irrigated corn, wheat and fruit trees combined (Milesi et al., 2005). It has been... R. Kerry, S. Shumate, B. Ingram, K. Hammond, D. Gunther, R. Jensen, S. Schill, N. Hansen, B. Hopkins |
8. Finnish Future Farm Speeding Up the Uptake of Precision AgricultureThe Finnish Future Farm (FFF) is an innovative concept that seamlessly integrates a physical Smart Farm with a Digital Twin, complemented by educational programs and business development opportunities. This holistic approach aims to propel the evolution of Smart Agriculture in Finland. At its core, FFF is a platform for co-creation with a strong emphasis on User-Centered Design. It employs a Multi-Actor Approach, bringing together companies, experts, researchers, and end users to collaborate... H.E. Haapala |
9. Effects of Crop Rotation on In-season Estimation of Optimal Nitrogen Rates for Corn Based on Proximal and Remote Sensing DataA remote sensing and calibration strip-based precision nitrogen (N) management (RS-CS-PNM) strategy has been developed by the Precision Agriculture Center at the University of Minnesota to provide in-season N recommendation rates based on satellite imagery. This strategy involves the application of multiple N rates before planting and the identification of the agronomic optimum N rate (AONR) at V7-V8 growth stages using normalized difference vegetation index (NDVI) calculated using satellite imagery.... A.C. Morales, D. . Quinn, K. Mizuta, Y. Miao |
10. Using Remote Sensing to Evaluate Cover Crop Performance and Plan Variable Rate ManagementThe adoption of cover crops (CC) in row-crop production, particularly in states like Indiana, has surged due to their recognized benefits in nutrient scavenging, soil health improvement, and erosion prevention. However, the spatial and temporal dynamics of CC performance pose challenges for efficient assessment and management. Traditional methods of quantifying CC production involve labor-intensive and time-consuming processes, creating a lag between data collection and decision-making for farmers.... S.A. Rubaino sosa, D. . Quinn, S. Armstrong |
11. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |
12. Report from Finland - How We Speed Up Innovation Uptake in Agriculture in FinlandFinnish agriculture is rapidly digitalizing. While the number of farms is decreasing, those that remain are increasingly adopting new technologies. Finns have a tradition of being early adopters of mobile technologies, with the Finnish phone company Nokia being a notable forerunner. However, in agriculture, users tend to be more conservative, resulting in lower than expected adoption rates of Precision Farming. The reasons for this are not only financial but also related to the usability issues... H.E. Haapala |