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.

Sixty-One Years Following Registration, Phorate Applied In-Furrow at Planting Suppresses Development of Late Leaf Spot on Peanut.

Late and early leaf spot are caused by Nothopassalora personata and Passalora arachidicola, respectively, and are damaging diseases of peanut (Arachis hypogaea L.) capable of defoliation and yield loss. Management of these diseases is most effective through the integration of tactics that reduce starting inoculum and prevent infection. The insecticide phorate was first registered in 1959 and has been used in peanut production for decades in-furrow at planting to suppress thrips. Phorate further provides significant suppression of Tomato spotted wilt virus infection beyond suppression of its thrips vector alone by activating defense-related responses in the peanut plant. From six experiments conducted from 2017 to 2019 in Blackville, SC, Reddick, FL, and Quincy, FL, significantly less leaf spot defoliation was exhibited on peanuts treated with phorate in-furrow at planting (26%) compared with nontreated checks (48%). In-season fungicides were excluded from five of the experiments, whereas the 2018 Quincy, FL, experiment included eight applications on a 15-day interval. Across individual experiments, significant suppression of defoliation caused by late leaf spot was observed from 64 to 147 days after planting. Although more variable within location-years, pod yield following phorate treatment was overall significantly greater than for nontreated peanut (2,330 compared with 2,030 kg/ha; P = 0.0794). The consistent defoliation suppression potential was estimated to confer an average potential net economic yield savings of $90 to $120 per hectare under analogous leaf spot defoliation. To our knowledge, these are the first data in the 61 years since its registration demonstrating significant suppression of leaf spot on peanut following application of phorate in-furrow at planting. Results support phorate use in peanut as an effective and economical tactic to incorporate to manage late and early leaf spot infections and development of fungicide resistance.

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.

Draft genome sequence data of Nothopassalora personata, peanut foliar pathogen from Argentina.

Late leaf spot (LLS) caused by the Ascomycete Nothopassalora personata (N.p.) (Syn. Cercosporidium personatum) is the main foliar disease of peanuts in Argentina and in peanut producing areas of the world, causing up to 70% yield losses. The extremely slow growth of this fungus in culture, that takes around one month to form a 1 cm colony (0.45 mm/day), and the lack of adequate young tissues from where to extract nucleic acids, have hindered genetic studies of this pathogen. Here, we report the first genome sequence of a N. personata isolate from South America, as well as genetic variants on its conserved genes, and the complete sequence of its mating-type locus MAT1-2 idiomorph. The N. personata isolate IPAVE 0302 was obtained from peanut leaves in Córdoba, Argentina. The whole genome sequencing of IPAVE 0302 was performed as paired end 150 bp NovaSeq 6000 and de novo assembled. Clean reads were mapped to the reference genome for this species NRRL 64463 and the genetic variants on highly conserved genes and throughout the genome were analyzed. Sequencing data were submitted to NCBI GenBank Bioproject PRJNA948451, accession number SRR23957761. Additional Fasta files are available from Harvard Dataverse (https://doi.org/10.7910/DVN/9AGPMG and https://doi.org/10.7910/DVN/YDO3V6). The data reported here will be the basis for the analysis of genetic diversity of the LLS pathogen of peanut in Argentina, information that is critical to make decisions on management strategies.

Use of image analysis to assess radial growth of Passalora arachidicola and Nothopassalora personata on solid media.

Despite significant research on early and late leaf spot diseases of peanut, in vitro study of the respective causal agents, Passalora arachidicola and Nothopassalora personata, has been limited due to cultural challenges that make growth of these fungi difficult to quantify with traditional methods. Studies were conducted to evaluate the practicality of image analysis to assess radial growth and tissue volume by correlating these assessments to dry mass. Image analysis was also used to estimate radial growth rates for these fungi over time. Tissue area and volume were significantly correlated to dry mass for P. arachidicola in two separate experiments, and for N. personata when medium had been removed from tissues prior to dry mass assessments. Tissue area densities were the same for P. arachidicola and Pseudocercospora smilacicola, evaluated as a nonstromatal cercosporoid comparison, whereas tissue volume densities were greater for P. archidicola and N. personata than P. smilacicola. A quadratic relationship was observed between radial growth and incubation time for all isolates evaluated. Growth rates of P. arachidicola isolates were 2 to 4 times faster than N. personata during the first week of incubation and slowed over time. Growth rates of NP18R, a phenotype variant of N. personata, increased after neighboring colonies met and was nearly 2.5 times faster than the fastest rates observed for P. arachidicola. These experiments demonstrate that when fungal tissues are observable, image analysis is a useful assessment tool for P. arachidicola and N. personata. Care should be taken to monitor fungal phenotypic changes in these species because phenotype degeneration can affect growth rates.

Transcriptome Responses of Wild Arachis to UV-C Exposure Reveal Genes Involved in General Plant Defense and Priming.

Stress priming is an important strategy for enhancing plant defense capacity to deal with environmental challenges and involves reprogrammed transcriptional responses. Although ultraviolet (UV) light exposure is a widely adopted approach to elicit stress memory and tolerance in plants, the molecular mechanisms underlying UV-mediated plant priming tolerance are not fully understood. Here, we investigated the changes in the global transcriptome profile of wild Arachis stenosperma leaves in response to UV-C exposure. A total of 5751 differentially expressed genes (DEGs) were identified, with the majority associated with cell signaling, protein dynamics, hormonal and transcriptional regulation, and secondary metabolic pathways. The expression profiles of DEGs known as indicators of priming state, such as transcription factors, transcriptional regulators and protein kinases, were further characterized. A meta-analysis, followed by qRT-PCR validation, identified 18 metaDEGs as being commonly regulated in response to UV and other primary stresses. These genes are involved in secondary metabolism, basal immunity, cell wall structure and integrity, and may constitute important players in the general defense processes and establishment of a priming state in A. stenosperma. Our findings contribute to a better understanding of transcriptional dynamics involved in wild Arachis adaptation to stressful conditions of their natural habitats.

Early Detection of Airborne Inoculum of Nothopassalora personata in Spore Trap Samples from Peanut Fields Using Quantitative PCR.

A quantitative PCR (qPCR)-assay was developed to detect airborne inoculum of Nothopassalora personata, causal agent of late leaf spot (LLS) on peanut, collected with a modified impaction spore trap. The qPCR assay was able to consistently detect as few as 10 spores with purified DNA and 25 spores based on crude DNA extraction from rods. In 2019, two spore traps were placed in two peanut fields with a history of LLS. Sampling units were replaced every 2 to 4 days and tested with the developed qPCR assay, while plots were monitored for symptom development. The system detected inoculum 35 to 56 days before visual symptoms developed in the field, with detection related to environmental parameters affecting pathogen life-cycle and disease development. This study develops the framework of the qPCR spore trap system and represents the initial steps towards validation of the performance of the system for use as a decision support tool to complement integrated management of LLS.

A SNP-Based Linkage Map Revealed QTLs for Resistance to Early and Late Leaf Spot Diseases in Peanut (Arachis hypogaea L.).

Cultivated peanut (Arachis hypogaea L.) is an important oilseed crop that is grown extensively in Africa, Asia and America. The diseases early and late leaf spot severely constrains peanut production worldwide. Because multiple genes control resistance to leaf spot diseases, conventional breeding is a time-consuming approach for pyramiding resistance genes into a single genotype. Marker-assisted selection (MAS) would complement and accelerate conventional breeding once molecular markers tightly associated with the resistance genes are identified. In this study, we have generated a large number of SNPs through genotyping by sequencing (GBS) and constructed a high-resolution map with an average distance of 1.34 cM among 2,753 SNP markers distributed on 20 linkage groups. QTL mapping has revealed that major QTL within a confidence interval could provide an efficient way to detect putative resistance genes. Analysis of the interval sequences has indicated that a major QTL for resistance to late leaf spot anchored by two NBS-LRR resistance genes on chromosome B05. Two major QTLs located on chromosomes A03 and B04 were associated with resistance genes for early leaf spot. Sequences within the confidence interval would facilitate identifying resistance genes and applying marker-assisted selection for resistance.