Abstract

Remote sensing has long been a part of precision agriculture research, with products related to spatially-explicit, within-field management of yield, disease, and harvest scheduling, to name a few application areas that are of interest to industry. A variety of spaceborne, airborne, and in-situ sensors are used to monitor crop health throughout the growing season, inform management decisions for farmers, and dictate logistical or planning operations. Crop yield forecasting, specifically, comes in many forms, but recently machine learning algorithms have gained prominence, at the cost of more traditional parametric regression models. While much work has been accomplished towards grain yield estimation using remote sensing data, silage yield forecasting has been less investigated. Furthermore, to date no sensor has been designed to optimize the spatial-spectral trade space that is relevant to yield estimation. We thus sought to identify the ideal spectral and spatial sampling parameters for a sensor to be used for this purpose. We first set out to identify a yield estimation algorithm which was favorable for both grain and silage yield, finding that support vector regression performed best at both field level and regional yield estimates for corn grain. We then leveraged three separate spectral band selection algorithms to quantitatively assess the most important spectral content found in our high spatial resolution, high spectral dimensional imaging spectroscopy data. These data were resampled both spectrally and spatially to determine the interplay of spatial resolution and spectral content on corn grain and silage yield estimation results. Corn grain yield estimation accuracies ranged from 7.02% mean absolute percentage error (MAPE) (root mean square error [RMSE] = 0.74 Mg/ha) at 0.06 m GSD to 11.45% MAPE (RMSE = 0.97 Mg/ha) at 30 m GSD using data collected at the same five band multispectral sampling configuration during the R4 (dough) growth stage. We found that this favorability extended to silage yield estimation, with MAPE ranging from 2.22-2.84% MAPE (1.38-1.52 Mg/ha) for 0.06-30 m GSD imagery. We thus were able to identify the spectral features which were ideal for this purpose using high resolution imaging spectroscopy data, while also investigating the changes in estimation accuracies as a function of spectral content and spatial resolution. Ultimately, the most favorable spectral configuration was multispectral in nature, containing blue (425 nm), green (510 nm), red (610 and 690 nm), and three near-infrared spectral bands (775, 810, 865, and 920 nm). This arguably is an ideal outcome, as sampling spectral content along these bandpasses greatly reduces dimensionality resulting from imaging spectroscopy data, while preserving the most important features for a machine learning regression algorithm to exploit. This spectral configuration was found to give the most accurate regression results, specifically centered at 8-16 m GSDs for corn silage yield, even though there were changes in accuracy across a wide range of spatial resolutions. We attributed this result to matching the semivariance range of the yield data, which in turn generated favorable modeling conditions for the support vector regression estimation. For corn grain, the average semivariance range was greater than our largest sampling distance, so in general the higher spatial resolutions below 1 m GSD were most accurate. These findings inform sensor engineers of the optimal spectral sampling configuration for an idealized sensor for use in corn grain or silage yield estimation at any spatial scale. Future work should address the underlying yield drivers behind these selections to further isolate the spectral content correlated to crop health and status metrics like nitrogen content, heat stress, water stress, and structural parameters. These are all correlated with yield and thus important to stakeholders, leading to potential mitigation via management techniques to improve yield projections.

Publication Date

8-30-2024

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Advisor

Jan van Aardt

Advisor/Committee Member

David Ross

Advisor/Committee Member

John Kerkes

Comments

This dissertation has been embargoed. The full-text will be available on or around 3/30/2025.

Campus

RIT – Main Campus

Download

Available for download on Tuesday, March 18, 2025

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