Proceedings
Authors
| Filter results6 paper(s) found. |
|---|
1. Beyond NDVI - Additional Benefits of RapidEye Image Products... U. Schulthess, K. Schelling |
2. Improvement of the Quality of “On-The-Go” Recorded Soil pHAn important basis for lime fertilisation is the recording of pH values. Many studies have shown that the pH value can vary greatly within a small area. Only through the development of a sensor by VERIS has it become possible to determine the pH value cheaply in a much higher sampling density than with the time and cost intensive laboratory method. With respect to their measurement principles, both methods differ fundamentally in that in the laboratory method an extraction medium is used. This... M. Schneider, T. Leithold, P. Wagner |
3. A Clustering Approach For Management Zone Delineation In Precision AgricultureIn recent years, an increasing amount of research has been devoted to the delineation of management zones. There have been quite a number of approaches towards using small-scale data for subdividing the field into a small number of zones, usually three or four. However, these zones are usually static, often require multi-year data sets and are based on low-resolution sampling methods for data acquisition. Furthermore, existing research into the... G. Ru, M. Schneider, R. Kruse |
4. From Rapideye's Spad In The Sky To N Application Maps... R. Schulthess, K. Schelling, D. Weist |
5. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth SeasonThis study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statistics... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang |
6. In-Field and Loading Crop: A Machine Learning Approach to Classify Machine Harvesting Operating ModeThis paper addresses the complex issue of classifying mode of operation (active, idle, stationary unloading, on-the-go unloading, turning) and coordinating agricultural machinery. Agricultural machinery operators must operate within a limited time window to optimize operational efficiency and reduce costs. Existing algorithms for classifying machinery operating modes often rely on heuristic methods. Examples include rules conditioned on machine speed, bearing angle and operational time... D. Buckmaster, J. Krogmeier, J. Evans, Y. Zhang, M. Glavin, D. Byrne, S.J. Harkin |