Proceedings
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| Filter results4 paper(s) found. |
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1. Economic Analysis Of Auto-swath Control For Alabama Crop ProductionWith the rising costs of fertilizer and pesticides and a push towards increasing environmental stewardship, farmers are seeking means to save money while preserving the environment and wildlife habitat. One technology that aids in remedying these concerns is auto-swath control. This investigation evaluates overlap savings using this technology on different application equipment and resulting in economic savings for those adopting it. Several field boundaries were obtained from across the state... D. Mullenix, A.M. Troesch, J.P. Fulton, A.T. Winstead, S.H. Norwood |
2. Application Rate Stability When Implementing Automatic Section Control Technology On Agricultural SprayersAutomatic section control (on and off) technology of sprayer boom sections is an intelligent solution to maximize spray application efficiency during field operations. This technology can reduce over-application of products. Spray controllers available with this technology attempt to maintain the set target rate by adjusting system flow rate based on ground speed and application width. Therefore, as sections are turned on or off, the flow regulating hardware must respond to maintain... A. Sharda, J.D. Luck, J.P. Fulton, S.A. Shearer, S.A. Shearer, D. Mullenix, M. Vanacht |
3. Tip Flow Uniformity When Using Different Automatic Section Control Technologies During Field OperationsAutomatic section control (ASC) technology provides a means to reduce double-coverage and application in unwanted areas thereby leading to input savings and improved environmental stewardship. However, the impact of ASC on spray boom dynamics and tip flow uniformity are unknown. Therefore, a study was conducted to evaluate tip flow rate uniformity and control system response in maintaining target application rates during field operation. Field experiments were conducted using two self-propelled... A. Sharda, J.D. Luck, J.P. Fulton, S.A. Shearer, T.P. Mcdonald, D. Mullenix |
4. 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 |