http://cv.baoxianyuan.cn/tag/smart-agriculture/Smart AgricultureHugo Blox Builder (https://hugoblox.com)en-usWed, 10 Jan 2024 00:00:00 +0000http://cv.baoxianyuan.cn/media/icon_hu4cd0055d70f3bb36a9713517df6e3d7a_12247_512x512_fill_lanczos_center_3.pnghttp://cv.baoxianyuan.cn/tag/smart-agriculture/http://cv.baoxianyuan.cn/project/smart-agriculture/Wed, 10 Jan 2024 00:00:00 +0000http://cv.baoxianyuan.cn/project/smart-agriculture/<h2 id="background">Background</h2> <p>Agricultural production is increasingly challenged by climate variability, water scarcity, and the need to enhance resource-use efficiency while maintaining food security. Improving irrigation and nutrient management has therefore become a central objective of sustainable agriculture. Traditional field experiments provide valuable insights into crop responses but are often limited in their ability to explore diverse environmental conditions and management scenarios.</p> <p>Recent advances in crop growth modeling, remote sensing, artificial intelligence, and precision agriculture offer new opportunities to integrate experimental observations with data-driven approaches. Process-based crop models can simulate physiological responses to environmental change, while machine learning techniques enable efficient prediction, optimization, and decision support using large and heterogeneous datasets.</p> <p>This project combines field experiments, crop modeling, and intelligent data analysis to develop digital solutions for sustainable agricultural management. Particular attention is given to improving water and nitrogen use efficiency, enhancing crop productivity, and supporting climate-resilient farming systems.</p> <h2 id="goals--scientific-questions">Goals &amp; Scientific Questions</h2> <p>The project aims to establish an intelligent agricultural framework that integrates process-based understanding with data-driven technologies for precision crop management.</p> <p>Key scientific questions include:</p> <ul> <li> <p>How can crop growth models and machine learning be effectively integrated to improve prediction accuracy and management decision-making?</p> </li> <li> <p>What are the optimal irrigation and nitrogen management strategies under varying environmental and climatic conditions?</p> </li> <li> <p>How do water and nutrient interactions influence crop growth, yield formation, and resource-use efficiency?</p> </li> <li> <p>Can data-driven approaches enhance the scalability and transferability of agricultural decision-support systems across different production environments?</p> </li> <li> <p>How can intelligent agricultural technologies contribute to sustainable food production under future climate and resource constraints?</p> </li> </ul>