A comparison of traditional and machine learning corn yield models using hyperspectral UAS and Landsat imagery

Abstract

The operationalization of precision agriculture imaging-based systems, especially in staple crops like maize (Zea mays L.), requires a quantitative comparison of yield forecast approaches toward improved crop management. Here, we compare the implementation of linear and exponential based sileage yield models for maize to machinelearning (ML) based yield models utilizing spaceborne multispectral imagery (MSI) and unmanned aerial system (UAS) collected hyperspectral imagery (HSI), respectively. We collected UAS imagery in a maize field in upstate New York at the V10 growth stage using a Headwall Nano-Hyperspec 272-band visible and near-infrared imaging system to test the accuracy a feed forward neural network yield estimation regression model as well as a support vector regression (SVR). Landsat imagery of the same field was collected over ten separate instances throughout the season for use in linear and exponential regressions, while ground truth sileage yield data were provided by an on-board yield monitor during harvest. The neural network regression response induced between 4.6-13% mean absolute error (MAE), the linear and exponential regression yielded a best performance of 5.5%, while the SVR model ranged from 1.16-4.56% MAE. These results bode well for future implementation of such silage maize yield modeling approaches leveraging hyperspectral data that include the spectral red edge. However, we suggest that model efficacy should be evaluated for use in other regions.