Master of Science / Department of Biological & Agricultural Engineering / Ajay Sharda / Discovered in 1971, sudden death syndrome (SDS), caused by the fungus Fusarium virguliforme, has spread from the US to South American and European countries. It has potential to infect soybean crops worldwide, causing yield losses of 10% to 15% and even 70% in extreme cases. There is a need for rapid spatial assessment of SDS. Currently, the extent and severity of SDS are scored using visual symptoms as indicators. This method can take hours to collect and is subject to human bias and changing environmental conditions. Color infrared (CIR) and thermal infrared (TIR) imagery detect changes in light reflectance (visible and near-infrared bands) and emittance (canopy temperature), respectively. Stressed crops may show deviations in light reflectiveness, as well as elevated canopy temperatures. The use of CIR and TIR imagery and flexible aerial remote sensing platforms offer an alternative for SDS detection and diagnosis compared to hand scoring methods.
Crop stress and diseases have been detected using manned and unmanned aerial systems previously. Yet, to date, SDS has not been remotely assessed using CIR or TIR imagery collected with aerial platforms. The following research utilizes high throughput CIR and TIR imagery collected using a small unmanned aerial system (sUAS) to detect and assess SDS. A comparative evaluation of ground-based and aerial CIR methods for assessing SDS was conducted to understand the effectiveness of novel aerial SDS detection methods. Furthermore, a TIR case study investigating the use of potential thermal canopy changes for SDS detection was conducted to investigate the possibility of using TIR as an SDS indicator.
CIR reflectance measured from a ground-based spectrometer and sUAS was collected data over a two-year period. Ground-based spectrometer data were collected weekly, while a sUAS collected aerial imagery late in the growing season each year before plant maturity. Pigment index (PI) values were derived from ground-based and aerial data. Results showed a strong negative correlation between SDS score and PI values. Aerial and ground-based data both showed strong correlations to SDS score, however, aerial data displayed a stronger relationship possibly due to minimal changes in environmental conditions. High SDS scores correlated strongly to aerial derived PI (R2 = 0.8359). Rapidly assessed high SDS allows for accurate screening of SDS critical for soybean breeding. The second year of the study investigated each component of SDS score, severity, and incidence. PI proved to have the best correlation with severity (R2 = 0.6313 and ρ = -0.8016) rather than incidence or SDS score. PI also correlated to SDS scores with R2 = 0.6159 and ρ = -0.7916.
A sUAS mounted TIR camera collected imagery four times during the growing season when SDS foliar symptoms were just starting to appear. At the start of the study period, the correlation between canopy temperature and SDS is low (ρ = -0.2907), but increases over the growing season as SDS prevalence increases ending with a strong correlation (ρ = -0.7158). Early identification of SDS leads to the implementation of mitigation practices and changes in irrigation scheduling before the disease reaches severe symptoms. Early mitigation of SDS reduces yield loses for farmers.
The use of both CIR and TIR aerial imagery captured using sUAS can provide rapid spatial assessments of SDS, which is required by both producers and plant breeders. PI derived from CIR imagery showing strong correlations to SDS score reinforce the idea of replacing the time-consuming traditional ground-based systems with the more flexible, faster, sUAS methods. TIR imagery was shown to be reliable in assessing SDS in soybeans further establishing another possible aerial method for early detection of SDS.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/38840 |
| Date | January 1900 |
| Creators | Hatton, Nicholle |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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