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1. 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 |
2. Economics Of Site Specific Liming - Comparison Of On-The-Go And Grid-Based Soil Sampling To Determine The Soil pHAn important base for adequate liming is the recording of the soil pH. Several studies indicated a large heterogeneity of soil pH within fields. Recently technological improvements facilitate an on-the-go determination of the soil pH in a much higher sampling density compared to the conventional, time consuming and costly laboratory method. The “Veris soil pH sensor” allows georeferenced on-the-go mapping of the soil pH. But the “Veris soil pH sensor” and... T. Leithold, P. Wagner |
3. 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 |
4. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |