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| Filter results7 paper(s) found. |
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1. Detection Of Nitrogen Deficiency In Potatoes Using Small Unmanned Aircraft SystemsSmall Unmanned Aircraft Systems (sUAS) are recognized as potentially important remote-sensing platforms for precision agriculture. A nitrogen rate experiment was established in 2013 with ‘Ranger Russet’ potatoes by applying four rates of nitrogen fertilizer (112, 224, 337, and 449 kg N/ha) in a randomized block design with 3 replicates. A Tetracam Hawkeye sUAS and Agricultural Digital Camera Lite sensor were used to collect imagery with near-infrared... D.A. Horneck, D.J. Gadler, A.E. Bruce, R.W. Turner, C.B. Spinelli, J.J. Brungardt, P.B. Hamm, E. Hunt |
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. An Automatic Control Method Research for 9YG-1.2 Large Round BalerWhen manual or semi-automatic round baler working, the tractor driver have to frequently manual the machine according to the bale process at the same time of driving. The driver easily feel fatigue in this operating mode for a long time, so the consistency of the bale’s density can not be guaranteed. And there may be wrong operation. In this article, we use the model 9YG-1.2 large round baler as a research prototype. We study the information collection and processing of the baler’s... J. Dong, Z. Meng, Y. Cong, A. Zhang, W. Fu, R. Pan, Q. Yang, Y. Shang |
4. 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 |
5. Delineation of Site-Specific Management Zones using Sensor-based Data for Precision N managementNitrogen is a critical nutrient influencing crop yield, but the common practice of uniform application of nitrogen fertilizer across a field often results in spatially variable nitrogen availability for the crop, leading to over-application in some areas and under-application in others. This imbalance can cause economic losses and significant environmental issues. Precision nitrogen application involves application of N fertilizers based on soil conditions and crop requirements. One approach for... R. Joshi, R. Khosla, D. Mandal, R. Unruh, W.A. Admasu |
6. Delineating Dynamic Variable Rate Irrigation Management ZonesAgriculture irrigation strategies have traditionally been made without accounting for the natural small-scale variability in the field, leading to uniform applications that often over-irrigate parts of the field that do not need as much water. The future success of irrigated agriculture depends on advancements in the capability to account for and leverage the natural variability in croplands for optimum irrigation management both in space and time. Variable Rate Irrigation (VRI) management offers... R. Unruh, W.A. Yilma, D. Mandal, R. Joshi, R. Khosla |
7. Analytics Model for Predicting Sucrose Percentage in Sugarcane Using Machine Learning TechniquesSucrose is one of the most important indicators in the final profitability of Colombian sugar mills, therefore, its understanding and forecast are fundamental for the business. In this work, a proposal is formulated for an analysis model that allows predicting the percentage of sucrose based on historical data from mechanically harvested farms with the objective of knowing the numerical value of sucrose for each month of milling and be able to plan monthly and annual sugar production. Regarding... P. Valencia ramirez |