Proceedings
Authors
| Filter results6 paper(s) found. |
|---|
1. Development Of An Enterprise Level Precision Agriculture SystemDevelopment of an Enterprise Level Precision Agriculture System James Ellingson, Chih Lai University of St. Thomas, School of Engineering 2115 Summit Ave, St. Paul, MN USA elli4729@stthomas.edu; Abstract – In this paper, a plan for the development of an Enterprise Level system for Precision Agriculture (PA) is described. The basic... J.L. Ellingson, B.K. Holub, S.E. Morgan, B.K. Werkmeister |
2. Integrated Approach to Site-specific Soil Fertility ManagementIn precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor |
3. Correlating Plant Nitrogen Status in Cotton with UAV Based Multispectral ImageryCotton is an indeterminate crop; therefore, fertility management has a major impact on the growth pattern and subsequent yield. Remote sensing has become a promising method of assessing in-season cotton N status in recent years with the adoption of reliable low-cost unmanned aerial vehicles (UAVs), high-resolution sensors and availability of advanced image processing software into the precision agriculture field. This study was conducted on a UGA Tifton campus farm located in Tifton, GA. The main... W. Porter, D. Daughtry, G. Harris, R. Noland, J. Snider, S. Virk |
4. Improving Site-specific Nutrient Management in the Southeastern US: Variable-rate Fertilization Based on Yield Goal by Management ZoneSite-specific nutrient management is a critical aspect of row crop production, especially when aiming to achieve improved yields in the highly variable fields in the Southeastern United States. Variable-rate (VR) fertilizer application is a common practice to implement site-specific nutrient management and relies heavily on the use of precision soil sampling methods (grid or zone) to obtain accurate information on spatial nutrient variability within the fields. Most fields in the southeastern... S. Virk, T. Colley, C. Kamerer, G. Harris, D. Beasley |
5. Detailed Derivation of Spatial Soil Attributes Using Soil Sensor Data, Terrain Analysis and Soil Maps with Supervised ClassificationDetailed knowledge of the spatial distribution of soils is critical for improved management and modeling in agriculture and forestry. However, information from existing soil maps is often not accurate enough and soil units are too large. In the current study, we used intensively collected information from soil profile analyses at the Scheyern site and used this as training data to map soil relationships on land in Dürnast with long-term fertilization experiments (BonaRes). Both... K. Heil |
6. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management ZonesInvestment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe |