Proceedings
Authors
| Filter results4 paper(s) found. |
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1. Multi-objective Optimization Analysis Model for County Range Soil Nutrients Sampling Point Layout Based on Improved Genetic AlgorithmThe layout of soil nutrients sampling points directly influence on the representative of soil samples and the precision of fertilization, also on sampling efficiency and sampling costs. By analyzing the various factors of county range farmland soil nutrients sampling, and setting the boundary conditions and objective function, the paper established multi-objective optimization... C. Tian'en |
2. A Decade of Precision Agriculture Impacts on Grain Yield and Yield VariationTargeting management practices and inputs with precision agriculture has high potential to meet some of the grand challenges of sustainability in the coming century, including simultaneously improving crop yields and reducing environmental impacts. Although the potential is high, few studies have documented long-term effects of precision agriculture on crop production and environmental quality. More specifically, long-term impacts of precision conservation practices such as cover crops, no-tillage,... M.A. Yost, N. Kitchen, K. Sudduth, S. Drummond, J. Sadler |
3. Precision Fall Urea Fertilizer Applications: Timing Impact on Carbon Dioxide, Ammonia Volatilization and Nitrous Oxide EmissionsTo minimize ammonia (NH3) volatilization and nitrous oxide (N2O) emissions from fall applied fertilizer, it is generally recommended to not apply the fertilizer until the soil temperature decreases below 10 C. However, this recommendation is not based on detailed measurements of NH3and N2O emissions. The objective of this study was to determine the influence of fertilizer application timing on nitrous oxide, carbon dioxide, and ammonia volatilization emissions. Nitrogen fertilizer was... S. Thies, D.E. Clay, S. Bruggeman, D. Joshi, S. Clay, J. Miller |
4. Embodied Agentic Artificial Intelligence for Precision Agriculture: Cross-domain Experience from Multimodal Generative AIMy team develops inclusive, responsible, and multimodal AI technology across education, healthcare, and digital services grounded in our research in embodied agentic intelligence and large language models. I will share deployed examples from these domains and draw parallels to agriculture, where similar technical challenges persist, ranging from multimodal fusion for contextual reasoning, explainable AI for actionable insights, and data-efficient learning for adaptation and localization. While... N. Chen |