About

Jeff Dunne is an Associate Professor in the Department of Crop and Soil Sciences at NC State University. His research focuses on peanut breeding, with particular emphasis on disease resistance, crop improvement, and innovative imaging techniques for crop health assessment. He has contributed to the development of automated pipelines for leaf spot severity scoring in peanuts using segmentation neural networks and has explored drone-based polarization imaging systems for disease detection in peanut plants. His work also includes evaluating germplasm for shade tolerance, studying the influence of cultivar and fungicide regimes on disease and yield, and enhancing peanut pre-sizing with advanced tracking algorithms. Jeff Dunne has authored numerous publications in peer-reviewed journals, advancing the understanding of peanut genetics, disease management, and crop phenotyping, thereby supporting sustainable peanut production and crop improvement.

Research topics

• Computer Science
• Biology
• Biotechnology
• Ecology
• Genetics
• Agronomy
• Economics
• Agroforestry
• Business
• Environmental science

Selected publications

• A Disillusioned Note on Biofortification Breeding

  Peanut Science · 2026-01-01

  articleOpen accessSenior author

  Folate (Vitamin B9) is an essential micronutrient for human health, and peanut (Arachis hypogaea L.) is a notable dietary source. Biofortification of peanut for elevated folate content could have significant public health implications, particularly in regions where dietary supplementation is inaccessible. This research note summarizes an exploratory effort to initiate folate biofortification in peanut and documents the substantial challenges encountered. Quantification of folate in mini core accessions via AOAC method 960.46 revealed unacceptably high within-line variation and inconsistent results across sources of the same accession, likely reflecting genetic heterogeneity rather than analytical failure. An economical ELISA-based approach produced results inconsistent with AOAC values and was deemed unsuitable without further methodological refinement. Two genome-wide association studies using the 58K and 48K Axiom Arachis SNP arrays failed to identify SNPs significantly associated with folate content among mini core accessions, likely due to limited sample size, long linkage disequilibrium blocks, and genetic heterogeneity within accessions. Three recombinant inbred line populations developed from crosses between high-folate mini core accessions and Georgia Green were successfully genotyped using a custom Tecan Allegro Targeted Genotyping panel; however, population structure analysis revealed all three populations reflected inbreeding of residually heterozygous female parents rather than true segregating RIL populations. These findings resulting in systemic F1 hybrid validation with molecular markers within our breeding program. We conclude that folate biofortification in peanut remains unfeasible until a reproducible, cost-effective, and scalable folate quantification method is developed, likely requiring multidisciplinary collaboration beyond the scope of traditional breeding programs.

  PublisherDOI

• Genetic Difference among Two Old Landrace Peanut Cultivars

  Peanut Science · 2026-01-01

  articleOpen access

  In the U.S. early peanut (Arachis hypogaea L) breeding programs began by making pure-line selections within mixed seed stock such as farmer saved seed and progressed toward hybridization-based programs. Some of the early imported seed stock came from Africa into the U.S. and became known as ‘Carolina African Runner’, ‘Southeastern Runner’, ‘North Carolina Runner’, ‘Wilmington Runner’, and ‘Virginia Runner’ depending upon where they were grown. The objective of this study was to compare two of these old landrace cultivars to determine possible genetic differences. The pure-line selections ‘Southeastern Runner 56-15’ and ‘North Carolina Runner No. 4’ were compared by genomic sequencing to ‘Tifrunner’ and ‘Bailey II’. Results from this study clearly shows these two landrace peanut cultivars were only 3% different or 97% genetically similar. However, both North Carolina Runner No. 4 and Southeastern Runner 56-15 were found to be 39.2% and 38.3% different from Bailey II, and are 37.5% and 35.5% different from Tifrunner, respectively. Results from this study suggest that these two older landrace cultivars are likely the same.

  PublisherDOI

• Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut

  The Plant Phenome Journal · 2025-05-21

  articleOpen accessSenior authorCorresponding

  Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, “visible atmospherically resistant index” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.

  PublisherOA PDFDOI

• Drone‐based polarization imaging system for leaf spot severity determination in peanut plants

  The Plant Phenome Journal · 2025-02-21 · 1 citations

  articleOpen access

  Abstract In this study, we introduce a new approach for enhancing peanut phenotyping through a polarization imaging platform. With leaf spot disease posing significant threats to peanut ( Arachis hypogae L.) crops, our research addresses the need for accurate and efficient detection methods. Polarization imaging offers unique advantages over more traditional spectral imaging solutions. Polarization correlates strongly with the geometric properties of an object, such as surface roughness or its orientation relative to the sensor or light source. Leveraging a drone‐based system, we conducted extensive field trials, collecting approximately 30,184 images over two growing seasons and locations. Images were processed for panchromatic (400–800 nm wavelengths) degree of linear polarization (DOLP) and compared to conventional red, green, and blue (RGB) imagery against conventional visual severity scores (modified nine‐point scale). Results indicated that when attempting to determine ground truth infection severity, panchromatic DOLP alone provided 1.34 root mean square error, RGB alone provided 1.09 root mean square error accuracy, and both modalities provided 1.03 root mean square error, indicating that adding a polarization capability can enhance or augment conventional RGB scoring pipelines. We expect that polarization may allow for phenotypic scoring models to mitigate—or leverage—confounding factors related to a leaf's geometric properties without the need for 3D imaging.

  PublisherOA PDFDOI

• Enhancing peanut pre‐sizing with PodTracker: A multiple pod tracking algorithm

  The Plant Phenome Journal · 2025-08-11

  articleOpen access

  Abstract In this work, we introduce novel computer vision and machine learning methods—including instance segmentation (Mask R‐CNN [Region‐Based Convolutional Neural Network]), multiple object tracking (PodTracker and DeepSortMask), and classification (Decision Tree)—to improve peanut pod pre‐sizing at grading stations and evaluate the sizing error associated with conventional pre‐sizer mechanical rollers. The trained Mask R‐CNN model achieved high bounding box average precision (AP) scores, with on Farmer Stock Fancy pods and on No. 1 pods. PodTracker (, where RMSE is root mean square error) outperformed DeepSortMask () in counting and tracking pods across frames. The classification model achieved an F1 score across No. 1, Fancy, and Jumbo pods, exhibiting lower standard deviations () compared to the conventional mechanical pre‐sizer (). These results demonstrate that low‐cost scanning solutions, when combined with advanced computer vision techniques, can be effectively integrated into conventional pre‐sizing machinery to offer more accurate sizing distributions than traditional methods.

  PublisherOA PDFDOI

• Registration of two <i>Arachis hypogaea</i> x <i>A. diogoi</i> introgression lines

  Journal of Plant Registrations · 2025-09-01

  articleOpen accessCorresponding

  Abstract Two (2 n = 4 x = 40) peanut ( Arachis hypogaea L.) germplasm lines, GP‐NC WS 18 (IL‐29) (Reg. no. GP‐254, PI 708341) and GP‐NC WS 19 (IL‐49) (Reg. no. GP‐255, PI 708342), originated from interspecific hybridization between Gregory (2 n = 4 x = 40; AABB genomes; PI 608666) and A. diogoi Hoehne (2 n = 2 x = 20; AA genome) (GKP 10602; PI 276235). The lines were developed in the peanut genetics program at North Carolina State University, Raleigh, NC. Fertility was restored by chromosome doubling to the hexaploid level with colchicine treatment, and after self‐pollinating progenies for 11 generations, fertile tetraploid progenies were recovered that are fully cross compatible with cultivated peanut. These two lines were tested extensively from 2016 through 2021 for resistance to leaf spots and Tomato spotted wilt virus (TSWV). GP‐NC WS 18 is highly resistant to both early and late leaf spots and to TSWV. GP‐NC WS 19 has resistance to the leaf spots and extremely high levels of resistance to TSWV. Furthermore, as A. diogoi is resistant to many diseases and insect pests (with only three being evaluated), the lines may prove valuable genetic resources for resistance to other diseases after additional evaluation. These two lines should provide unique, improved germplasm for breeders interested in multiple disease resistance and in expanding the germplasm pool of A. hypogaea .

  PublisherOA PDFDOI

• Influence of Virginia Market-Type Cultivar and Fungicide Regime on Leaf Spot Disease and Peanut Yield in North Carolina

  Agronomy · 2025-07-18 · 1 citations

  articleOpen access

  Determining the effectiveness of fungicide programs based on cultivar resistance to pathogens, especially late leaf spot (caused by Nothopassalora personata (Berk. &amp; M.A. Curtis) [U. Braun, C. Nakash., Videira &amp; Crous]) is important in establishing recommendations to peanut (Arachis hypogaea L.) farmers. Research was conducted in North Carolina during 2021 and 2022 at three locations to compare the incidence of late leaf spot (e.g., visual estimates of percent of peanut leaflets with lesions), percentage of the peanut canopy defoliated caused by this disease, and yield of the peanut cultivars Bailey II, Emery, and Sullivan when exposed to five fungicide regimens including a non-treated control. Peanut yield was not affected by the interaction of cultivar × fungicide regimens. While differences in leaf spot incidence and canopy defoliation were noted for cultivars, these differences did not translate into differences in peanut yield. All fungicides regimens protected peanut yield from leaf spot disease regardless of the number of sprays during the cropping cycle (e.g., three, four, or five sprays). Peanut yield in the absence of fungicides was 4410 kg/ha compared with a range of 5000 to 5390 kg/ha when fungicides were applied. Peanut yield was greater when fungicides were applied four or five times compared with only three sprays or non-treated peanut. The regimen with five consecutive sprays of chlorothalonil alone for the first and final spray in the regimen and when this fungicide was applied with tebuconazole for the second, third, and fourth sprays was as effective as fungicide regimens including combinations of pydiflumetofen plus azoxystrobin plus benzovindiflupyr, mefentrifluconazole plus pyraclostrobin plus fluxapyroxad, bixafen plus flutriafol, and prothioconazole plus tebuconazole.

  PublisherOA PDFDOI

• Influence of crop sequence, cultivar, and metam sodium on plant‐parasitic nematode population and peanut

  Crop Forage & Turfgrass Management · 2025-07-09

  articleOpen access

  Abstract Prior cropping sequence can have a major effect on populations of plant parasitic nematodes (PPN), peanut ( Arachis hypogaea L.) yield, and financial return at the farm level. Effective crop rotation sequences can reduce PPN populations and reduce grower reliance on nematicides or fumigant. Recently, the root‐knot nematode‐resistant cultivar TifNV‐High O/L was released. To determine the performance of this cultivar to PPNs in North Carolina, a cropping system trial that included 10 diverse rotation sequences from 2013–2020 was used that included rotation sequences that were favorable or unfavorable for maximum peanut yield. Peanut was planted in 2021 to determine the residual effects of the previous cropping sequence. Cropping sequence, cultivar, and metam sodium impacted peanut yield and population of PPN in soil. Fewer root‐knot nematodes ( Meloidogyne spp.) and less root injury from nematode feeding were observed for the cultivar TifNV‐High O/L than Bailey II. Metam sodium decreased populations of lesion ( Pratylenchus brachyurus Filipjev &amp; Schuurmans‐Stekhoven), ring ( Mesocriconema ornatum Raski), root‐knot, and soybean cyst ( Heterodera glycines Ichinohe) nematodes in soil. With the exception of lesion nematode, response of nematodes and peanut to crop sequence, cultivar, and metam sodium was independent.

  PublisherOA PDFDOI

• Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks

  Plant Methods · 2025-02-20 · 1 citations

  articleOpen access

  BACKGROUND: Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity. RESULTS: The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to expert visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot. CONCLUSION: Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.

  PublisherOA PDFDOI

• Previous Cropping Sequence Affects Plant-Parasitic Nematodes and Yield of Peanut and Cotton More than Continuous Use of Fluopyram

  Crops · 2025-03-20 · 3 citations

  articleOpen access

  Cropping sequence can have a major impact on diseases, pests, nutrient cycling, crop yield, and overall financial return at the farm level for crops that are grown on an annual basis. In some cases, implementing an effective rotation sequence can allow growers to avoid using nematicides to suppress plant-parasitic nematodes. Two cropping system trials were established with ten rotations each in 1997 and have been maintained through 2022. From 2013 through 2019, rotation sequences were both favorable and unfavorable for peanut (Arachis hypogaea L.) plant health. Peanut (2020), cotton (Gossypium hirsutum L.) (2021), peanut (2022), and corn (Zea mays L.) (2023) were planted in all plots to determine the residual effects of the previous cropping sequence. In 2020, 2021, and 2022, fluopyram at 0.25 kg ai/ha was applied in the seed furrow at planting in the same area of each plot to determine if the response of nematode populations and crop yield to this nematicide differed based on previous crop sequence. Differences in nematode populations in soil and yield of peanut (2020 and 2022) and cotton (2021) were observed when comparing crop rotation sequences regardless of fluopyram treatment. Increasing the number of years peanut was in the rotation or including soybean [Glycine max (L.) Merr.] rather than corn or cotton often resulted in higher populations of nematodes and a lower peanut yield. While fluopyram occasionally reduced nematode populations in soil and root injury from nematode feeding, the yield of peanut did not differ when comparing non-treated and fluopyram-treated peanut. When pooled over crop rotation sequence, peanut yield at Lewiston–Woodville was 5970 kg/ha vs. 6140 kg/ha for these respective treatments. At this location in 2021 and at Rocky Mount in 2019 and 2020, peanut yield for this comparison was 4710 vs. 4550, 5790 kg/ha vs. 6010 kg/ha, and 6060 kg/ha vs. 6120 kg/ha, respectively. These data indicate that previous crop sequences can influence crop yield more than the continuous use of fluopyram. Therefore, fluopyram is not recommended for application in the seed furrow at planting to suppress nematodes in cotton or peanut in North Carolina.

  PublisherOA PDFDOI

Frequent coauthors

• Ryan J. Andres

  North Carolina State University

  12 shared

• Susana R. Milla‐Lewis

  North Carolina State University

  11 shared

• Rick Brandenburg

  North Carolina State University

  8 shared

• Consuelo Arellano

  North Carolina State University

  8 shared

• Eric A. L. Jones

  South Dakota State University

  7 shared

• Robert Austin

  North Carolina State University

  6 shared

• Grady L. Miller

  North Carolina State University

  6 shared

• Ramón G. León

  North Carolina State University

  6 shared

Labs

• Peanut Breeding Method Road UnitPI