###### Abstract  
Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = \(-\)0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59-0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train-test data split ratios (50:50-95:5). The ML models and train-test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84-0.97, rRMSE: 8.72-20%), while a higher train-test split ratio lowered the performances of the ML models. The 95:5 train-test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity. Keywords:SUAS multispectral sensing; machine learning; soybean yield; vegetation index; precision management +
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infestation is conventionally assessed from visible features of crop response by visual scouting and depends on human experience. This process is laborious, time-consuming, expensive, and most prone to misdiagnosis [6]. In addition, the visible symptoms may often be confused by the observers with other biotic or abiotic stressors including nutrient, water, and other fungal or pest infestations. Destructive laboratory analysis of soil and root samples is also conducted to ascertain the degree of SCN infestation [5]. Pertaining to the soil analysis method, soil samples are collected from the field and brought to the taxonomic/pathology laboratories. The samples are cleaned thoroughly, and specimen slides of the cleaned samples are prepared to be observed under the microscope. The nematodes are then visually-manually counted by trained nematologists to identify the degree of infestation [7]. Although accurate, this method is again laborious, time taking and most importantly, lacks geospatial sampling accuracy. During peak seasons and shortage of taxonomic experts, the lab results to the growers can be delayed which leads into missed opportunities of prompt and precise control of SCN infestations in the field. Given the challenges associated with conventional impact assessment techniques and rapid phenotyping, remote sensing (using aerial multispectral imagery) is a possible solution for quantifying crop stressors [8; 9]. Remote sensing offers a quick and economical way to identify issues that arise during a cropping season to deploy appropriate and timely management practices. For example, [PERSON] et al. [10] was able to distinguish plants that were healthy, and the ones infected with nematodes and a soil-borne fungal pathogen using spectral reflectance and vegetation indices (VIs). A correlation between disease rating and VI of (>0.8) was observed using stepwise linear discriminant analysis (LDA) and logistic discriminant analysis (LgDA). Based on the plant spectra, a two-class discriminant model was able to identify 58% of the infested plants and 97% of the healthy plants with a disease. A study by [PERSON] et al.