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- W4385236469 abstract "Soybean Cyst Nematode (SCN) is a serious pathogen in soybean production and contributes to annual economic losses of more than $1.5 billion (1996–2016) in the U.S. SCN is a microscopic thread-like nematode that burrows into the roots of soybean plants and typically cannot be identified above ground. The paper investigates multitude of variables such as NDVI from multi-spectral images, egg counts, and micro-nutrient composition (e.g., pH, nitrogen, phosphorus, potassium) across two SCN-prone field plots in Casselton/Prosper, North Dakota. The preliminary results indicate that NDVI is a good metric to track for SCN density population during planting, growing, and harvesting periods along with other historical ground truth data. Also, a contour plot using Empirical Bayesian Kriging (EBK) was designed by integrating NDVI and egg count data for co-tracking distribution changes. Such access to ground truth data (i.e., aerial and soil properties) will enable the development and training of robust machine learning models for predicting SCN hotspots." @default.
- W4385236469 created "2023-07-26" @default.
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- W4385236469 date "2023-05-18" @default.
- W4385236469 modified "2023-09-27" @default.
- W4385236469 title "Correlation of Egg counts, Micro-nutrients, and NDVI Distribution for Accurate Tracking of SCN Population Density Detection" @default.
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- W4385236469 doi "https://doi.org/10.1109/eit57321.2023.10187314" @default.
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