Sensor-Based Quantification of Peanut Disease Defoliation Using an Unmanned Aircraft System and Multispectral Imagery.

Early leaf spot (Passalora arachidicola) and late leaf spot (Nothopassalora personata) are two of the most economically important foliar fungal diseases of peanut, often requiring seven to eight fungicide applications to protect against defoliation and yield loss. Rust (Puccinia arachidis) may also cause significant defoliation depending on season and location. Sensor technologies are increasingly being utilized to objectively monitor plant disease epidemics for research and supporting integrated management decisions. This study aimed to develop an algorithm to quantify peanut disease defoliation using multispectral imagery captured by an unmanned aircraft system. The algorithm combined the Green Normalized Difference Vegetation Index and the Modified Soil-Adjusted Vegetation Index and included calibration to site-specific peak canopy growth. Beta regression was used to train a model for percent net defoliation with observed visual estimations of the variety 'GA-06G' (0 to 95%) as the target and imagery as the predictor (train: pseudo-R2 = 0.71, test k-fold cross-validation: R2 = 0.84 and RMSE = 4.0%). The model performed well on new data from two field trials not included in model training that compared 25 (R2 = 0.79, RMSE = 3.7%) and seven (R2 = 0.87, RMSE = 9.4%) fungicide programs. This objective method of assessing mid-to-late season disease severity can be used to assist growers with harvest decisions and researchers with reproducible assessment of field experiments. This model will be integrated into future work with proximal ground sensors for pathogen identification and early season disease detection.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Epidemiology of Soybean Rust in Soybean Sentinel Plots in Florida.

Since its discovery in the southeastern United States in 2004, soybean rust (SBR) has been variable from year to year. Caused by Phakopsora pachyrhizi, SBR epidemics in Florida are important to understand, as they may serve as an inoculum source for other areas of the country. This study examined the first disease detection date, incidence, and severity of SBR in relation to environmental data, growth stage, and maturity group (MG3, MG5, MG7) in soybean sentinel plots (225 m2) across north Florida from 2005 through 2008. The majority (91%) of the initial infections were observed in MG5 and MG7 soybeans, with plots not becoming infected until growth stage R4 or later. Precipitation was the principle factor affecting disease progress, where disease increased rapidly after rain events and was suppressed during dry periods. On average, plots became infected 30 days earlier in 2008 than 2005. In 2008, there was a significant increase in disease incidence and severity associated with the occurrence of Tropical Storm Fay, which deposited up to 380 mm of rainfall in north Florida. The results of this study indicate that climatic and environmental factors are important in determining the development of SBR in north Florida.