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Kim, D
Tian, L.F
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Authors
Zhao, Y
Li, L
Ting, K.C
Tian, L.F
Ahamed, T
Li, L
Jiang, D
Campos, R.P
Lu, Z
Tian, L.F
Kim, D
Topics
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Type
Oral
Poster
Year
2014
2025
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Filter results3 paper(s) found.

1. Near-Real-Time Remote Sensing And Yield Monitoring Of Biomass Crops

The demand for bioenergy crops production has increased tremendously by the biofuel industry for substitution of traditional fuels due to the economic availability and environmental benefits. Pre-Harvest monitoring of biomass production is necessary to develop optimized instrumentation and data processing systems for crop growth, health and stress monitoring; and to develop algorithms for field operation scheduling. To cope with the problems of missing critical... Y. Zhao, L. Li, K.C. Ting, L.F. Tian, T. Ahamed

2. Field-Based High-Throughput Phenotyping Approach For Soybean Plant Improvement

The continued development of new, high yielding cultivars needed to meet the world’s growing food demands will be aided by improving the technology to rapidly phenotype potential cultivars. High-throughput phenotyping (HTP) is essential to maximize the greatest value of genetics analysis and to better understand the plant biology and physiology in view of a “Feed the World in 2050” theme. Field-based high-throughput phenotyping platform... L. Li, D. Jiang, R.P. Campos, Z. Lu, L.F. Tian

3. Optimizing Frost Prediction with a Multi-Window CNN–XGBoost Soft-Voting Ensemble

Recent 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