Proceedings
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| Filter results4 paper(s) found. |
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1. Use Of Quality And Quantity Information Towards Evaluating The Importance Of Independent Variables In Yield PredictionYield predictions based on remotely sensed data are not always accurate. Adding meteorological and other data can help, but may also result in over-fitting. Working with American Crystal Sugar, we were able to demonstrate that the relevance of independent variables can be tested much more reliably when not only yield but also quality attributes are known, such as the sugar content and the sugar... E. Momsen, J. Xu, D.W. Franzen, J.F. Nowatzki, K. Farahmand, A.M. Denton |
2. Surplus Science and a Non-linear Model for the Development of Precision Agriculture TechnologyThe advent of ‘big data technologies’ such as hyperspectral imaging means that Precision Agriculture (PA) developers now have access to superabundant and highly heterogeneous data. The authors explore the limitations of the classic science model in this situation and propose a new non-linear process that is not based on the premise of controlled data scarcity. The study followed a science team tasked with developing highly advanced hyperspectral techniques for a ‘low... M.Z. Cushnahan, I.J. Yule, B.A. Wood, R. Wilson |
3. Window-based Regression Analysis of Field DataHigh-resolution satellite and areal imagery enables multi-scale analysis that has previously been impossible. We consider the task of localized linear regression and show that window-based techniques can return results at different length scales with very high efficiency. The ability of inspecting multiple length scales is important for distinguishing factors that vary over different length scales. For example, variations in fertilization are expected to occur on shorter length... A.M. Denton, H. Chavan, D.W. Franzen, J.F. Nowatzki |
4. Optimizing Frost Prediction with a Multi-Window CNN–XGBoost Soft-Voting EnsembleRecent global climate change has increased the frequency of late-spring frost events, causing more severe and widespread damage to orchard growers. Frost formation occurs due to rapid temperature drops over short periods combined with overnight air stagnation; thus, effective prediction requires analyzing patterns across multiple time scales. We introduce a hybrid frost-forecasting framework that combines a multi-window 1-D convolutional neural network (CNN), utilizing 6-, 12-, and 24-hour... D. Kim |