###### Abstract
[6] showed that hyperspectral imaging is useful in detecting and discriminating root disease development (_Heterodera schuchtii_ and _Rhizoctonia solani_) in sugar beet. The AISA and HyMap data showed a classification accuracy of 72% and 64%, respectively, from a supervised classification using a Spectral Angle Mapper of the organism-induced leaf symptoms. In addition to SCN management, estimation of crop yield prior to season end is a challenging task that requires significant time investments involving the collection of complicated information of crop genotype, crop phenotypes, environmental factors (weather and soil) as well as management practices, among others [2]. Pre-harvest yield estimation much ahead enables growers to deploy potential management practices such as fertilizers, irrigation, pesticides, and herbicides to achieve a better yield at the season end. Season-end yield is typically recorded using yield monitoring systems on grain combines which have been in use for more than 25 years [11]. Such yield monitoring devices are invasive, expensive to own and operate, demand frequent field calibration, and require data cleaning for retrieving useful information [11; 12]. Furthermore, SY is not only influenced by inherent physiological and structural traits but also by environmental factors like soil patterns, climate, and hydrology, making manual monitoring labor-intensive and typically ineffective [13]. Numerous methods for non-invasive estimation of crop yield prior to season end have also been developed which include but are not limited to statistical, agro-meteorological, as well as crop growth models [14]. Typically, crop growth models are built on a regional scale for growth monitoring and yield estimation, feeding on reginal weather data or low-resolution satellite remote sensing inputs [15]. These models fail to account for the temporal and spatial variations in meteorological data, crop parameters (LAI, biomass), and soil characteristics. For example, [PERSON] et al. [15] estimated wheat yields using Sentinel-2 remote sensing data, crop growth model of SAFY and the results showed an R\({}^{2}\) of 0.73, 0.83 and 0.49 as well as RMSE of 0.72, 1.13 and 1.14 t/ha for correlation between vegetation indices, leaf area index (LAI), biomass, and yield. The MODIS satellite imagery-derived LAI was integrated into the BEPS crop growth model, which improved the accuracy of corn yield estimation [15]. Specific to soybeans, CROPGRO and SOYGRO models have been used to estimate biomass and SY using LAI and VIs and inputs [16]. [PERSON] et al. [17] used the AQUACROP model to estimate soybean seed yield with the model providing a satisfactory performance (R\({}^{2}\) = 0.99 and low RMSE of 0.10 t ha\({}^{-1}\)). Inputs for the AQUACROP model were environmental conditions (air temperature, relative humidity, rainfall among others) and crop parameters such as dry aboveground biomass, root length and canopy cover. Most crop growth models are designed for regional scale estimation, limiting spatiotemporal variability assessments [15]. As discussed earlier, techniques that can quantify crop growth and yield at high-throughput scale but are also able to provide spatiotemporal variations at high resolution are needed [15]. Agricultural productivity has been estimated using a range of remote sensing data inputs in various data-driven models [1,6]. These inputs come from the ground, small unmanned aircraft systems (SUAS), and high-altitude (satellite, aerospace) platforms. High altitude remote sensing, like satellites, are appropriate for large scale operations but are prone to be affected by weather and offer low resolution. Ground-based remote sensing (analytical spectral devices) offers high resolution and are easy to use but are labor-intensive, time-consuming, and have a low operation efficiency for monitoring a larger region [18]. SUAS-based remote sensing applications have rapidly advanced for agricultural systems due to their ability to overcome the limitations of satellites and ground remote sensing.