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.

Peanut disease epidemiology under dynamic microclimate conditions and management practices in North Florida.

Diverse field characteristics, weather patterns, and management practices can result in variable microclimates. The objective was to relate in-field microclimate conditions with peanut diseases and yield and determine the effect of irrigation and fungicides within these environments. Irrigation did not have a major impact on disease and yield. Stem rot (Athelia rolfsii) and early (Passalora arachidicola) and late (Nothopassalora personata) leaf spot were most affected by changes in environmental patterns across seasons. Average non-treated stem rot was 12.9% in 2017 which dropped considerably in 2018 to 0.2% but emerged again in 2019 to 3.2%. Stem rot incidence varied across the field, and the response to fungicides depended on management zone. Leaf spot defoliation in non-treated plots was severe in 2019 reaching an average of 73% at 126 days after planting but only reached 15% in 2017 and 35% in 2019 at the same stage. A low-input fungicide schedule was able to reduce foliar disease in all zones and seasons, but the microclimatic conditions in the low-lying area favored leaf spot in 2017 and 2018 although not in the dryer 2019 season. Seasonal differences in disease and plant growth affected the level of protection against average yield loss using a standard low-input program which in 2017 (527 kg/ha) was not as great as 2018 (2,235 kg/ha) or 2019 (1,763 kg/ha). Disease prediction models built on dynamic environmental factors in the context of multiple pathogens and natural field conditions could be developed to improve within-season management decisions for more efficient fungicide inputs.

Relating Peanut Rx Risk Factors to Epidemics of Early and Late Leaf Spot of Peanut.

Previous research has demonstrated the efficacy of prescription fungicide programs, based upon Peanut Rx, to reduce combined effects of early leaf spot (ELS), caused by Passalora arachidicola (Cercospora arachidicola), and late leaf spot (LLS), caused by Nothopassalora personata (syn. Cercosporidium personatum), but the potential of Peanut Rx to predict each disease has never been formally evaluated. From 2010 to 2016, non-fungicide-treated peanut plots in Georgia and Florida were sampled to monitor the development of ELS and LLS. This resulted in 168 cases (unique combinations of Peanut Rx risk factors) with associated total leaf spot risk points ranging from 40 to 100. Defoliation ranged from 13.9 to 100%, and increased significantly with increasing total risk points (conditional R2 = 0.56; P < 0.001). Leaf spot onset (time in days after planting [DAP] when either leaf spot reached 1% lesion incidence), ELS onset, and LLS onset ranged from 29 to 140, 29 to 142, and 50 to 143 DAP, respectively, and decreased significantly with increasing risk points. Standardized AUDPC of ELS was significantly affected by risk points (conditional R2 = 0.53, P < 0.001), but not for LLS. After removing redundant Peanut Rx factors, planting date, rotation, historical leaf spot prevalence, cultivar, and field history were used as fixed effects in mixed effect regression models to evaluate their contribution to leaf spot, ELS or LLS prediction. Results from mixed effects regression confirmed that the selected Peanut Rx risk factors contributed to the variability of at least one measurement of development of combined or separate epidemics of ELS and LLS, but not all factors affected ELS and LLS equally. Historical leaf spot prevalence, a new potential preplant risk factor, was a consistent predictor of the dominant disease(s) observed in the field. Results presented here demonstrate that Peanut Rx is a very effective tool for predicting leaf spot onset regardless of which leaf spot is predominant, but also suggest that associated risk does not reflect the same development for each disease. These data will be useful for refining thresholds for differentiating high, moderate, and low risk fields, and reevaluating the timing of fungicide applications in reduced input programs with respect to disease onset.