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Correlation of Egg counts, Micro-nutrients, and NDVI Distribution for Accurate Tracking of SCN Population Density Detection
Anton Skurdal (University of North Dakota), Youness Arjoune (University of North Dakota), Niroop Sugunaraj (University of North Dakota), Shree Ram Abayankar Balaji (University of North Dakota), Sreejith Vidhyadharan (University of North Dakota), Prakash Ranganathan (University of North Dakota), Burton L. Johnson (North Dakota State University)
Résumé : 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.