Proceedings
Authors
| Filter results4 paper(s) found. |
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1. Processing Yield Data from Two or More CombinesErroneous data affect the quality of yield map. Data from combines working close to each other may differ widely if one of the monitors is not properly calibrated and this difference has to be adjusted before generating the map. The objective of this work was to develop a method to correct the yield data when running two or more combines in which at least one has the monitor not properly calibrated. The passes of each combine were initially identified and three methods to correct yield data were... L. Maldaner, J.P. Molin, T.F. Canata |
2. Static and Kinematic Tests for Determining Spreaders Effective WidthSpinner box spreaders are intensively used in Brazil for variable rate applications of lime in agriculture. The control of that operation is a challenging issue because of the complexity involved on the interactions between product and machine. Quantification of transverse distribution of solids thrown from the spinner box spreaders involves dynamic conditions tests where the material deposited on trays is evaluated along the pass of the machinery. There is a need of alternative testing methods... L. Maldaner, T. Canata, J. Molin, B. Passalaqua, J.J. Quirós |
3. Identifying and Filtering Out Outliers in Spatial DatasetsOutliers present in the dataset is harmful to the information quality contained in the map and may lead to wrong interpretations, even if the number of outliers to the total data collected is small. Thus, before any analysis, it is extremely important to remove these errors. This work proposes a sequential process model capable of identifying outlier data when compared their neighbors using statistical parameters. First, limits are determined based on the median range of the values of all the... L. Maldaner, J. Molin, T. Tavares, L. Mendez, L. Corrêdo, C. Duarte |
4. Precision Agriculture Research Infrastructure for Sustainable FarmingPrecision agriculture is an emerging area at the intersection of engineering and agriculture, with the goal of intelligently managing crops at a microscale to maximize yield while minimizing necessary resource. Achieving these goals requires sensors and systems with predictive models to constantly monitor crop and environment status. Large datasets from various sensors are critical in developing predictive models which can optimally manage necessary resources. Initial experiments at University... C. Lai, C. Min, R. Chiang, A. Hafferman, S. Morgan |