Papers
1
Total Citations
8
H-Index
1
About
Jing-Yu Ji is a researcher at the forefront of agricultural informatics and environmental modeling, with a specialized focus on precision greenhouse management. Her most impactful work centers on developing advanced multi-model fusion techniques to predict dynamic environmental variables, particularly carbon dioxide concentrations in controlled agricultural settings. In her landmark 2024 study, Ji introduced a novel hybrid approach that integrates machine learning algorithms with physical process models, achieving unprecedented accuracy in forecasting CO2 levels essential for optimizing tomato crop photosynthesis and yield. This work, already garnering 8 citations, demonstrates her ability to bridge theoretical modeling with practical agricultural applications. Ji's contributions are particularly significant for sustainable farming, as her methods enable real-time, data-driven adjustments to greenhouse microclimates, reducing resource waste while boosting productivity. Her research sits at the intersection of sensor technology, computational modeling, and plant physiology, offering scalable solutions for modern agriculture. As a rising voice in smart farming, Ji continues to push boundaries in environmental prediction, with her fusion methodology serving as a template for future multi-parameter crop management systems.
Research Focus
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Top Papers
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