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Balint-Kurti, P.

Publications and source records attributed to Balint-Kurti, P..

5 recordsLinked to original sources
bioRxiv2026

Aerial imagery and deep learning accurately estimate maize foliar disease severity

Southern leaf blight (SLB) is a foliar disease of maize (Zea mays L.) caused by the necrotrophic fungal pathogen Cochliobolus heterostrophus. Genetic resistance is the most effective control method for SLB. Developing disease resistant maize lines requires field trials during which disease phenotypes must be visually assessed. Remote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity that is otherwise labor-intensive and subjective. This project used a deep learning approach to estimate SLB disease severity of single-row maize plots from drone imagery. Over 26,000 plot-level images produced from flights conducted across three growing seasons were labeled with in-field visual scores taken contemporaneously by expert raters. Variation in environmental conditions contributed to a labeled image dataset that reflects the complexity of agronomic field experiments. We assessed the ability of nine deep learning models from three architectural families to estimate disease severity. The best-performing model, EVA-02-B, achieved strong cross year generalization (R2 = 0.697). Error analysis found that performance was more strongly associated with seasonal disease progression and flight-score time offset than with image-level noise. UAV-based deep learning estimated SLB severity with comparable precision to expert raters. This study lays the groundwork for integrating automated phenotypes into genetic studies of disease resistance. PLAIN LANGUAGE SUMMARYSouthern leaf blight (SLB) of maize is a disease that causes yield loss worldwide and developing resistant varieties offers the best hope for controlling the disease. Studying SLB resistance requires plant pathologists to visually score severity in the field, a labor-intensive method that requires expertise. To address these challenges, we asked whether SLB severity scoring could be automated using drone images and artificial intelligence (AI). We trained AI models using three years of image and score data then compared the results to visual scores taken by five plant pathologists. The best performing AI model showed a similar level of consistency to the experts and proved capable of scoring severity despite unpredictable and uncontrollable conditions that affect field imaging experiments such as weeds or shadows. These findings provide a validated method that improves the efficiency of maize disease research, a critical area of study for agricultural sustainability and productivity.

plant biology↗
bioRxiv2025

A maize near-isogenic line population designed for gene discovery and characterization of allelic effects

In this study we characterized a panel of 1,264 maize near-isogenic lines (NILs), developed from crosses between 18 diverse inbred lines and the recurrent parent B73, referred to as nested NILs (nNILs). 884 of the nNILs were genotyped using genotyping-by-sequencing (GBS). Subsequently, 24 of these nNILs, and all the parental lines, were re-genotyped using a high-density SNP chip. A novel pipeline for calling introgressions, which does not rely on knowing the donor parent of each nNIL, was developed based on a hidden Markov model (HMM) algorithm. By comparing the introgressions detected using GBS data with those identified using chip data, we optimized the HMM parameters for analyzing the entire nNIL population. A total of 2,972 introgressions were identified across the 884 nNILs. Individual introgression blocks ranged from 21 bp to 204 Mbp, with an average size of 17 Mbp. By comparing SNP genotypes within introgressed segments to the known genotypes of the donor lines we determined that in about one third of the lines, the identity of the donors did not match expectation based on their pedigrees. We characterized the entire nNIL population for three foliar diseases. Using these data, we mapped a number of quantitative trait loci (QTL) for disease resistance in the nNIL population and observed extensive variation in effects among the alleles from different donor parents at most QTL identified. This population will be of significant utility for dissecting complex agronomic traits and allelic series in maize. Significance StatementThe study reports the characterization of a publicly available population of 1,264 maize near-isogenic lines largely derived from a single recurrent parent and 18 donor lines. This population is likely to be of significant utility for the characterization of allelic series at loci of interest.

plant biology↗
bioRxiv2024

The interaction between abiotic and biotic soil factors drive heterosis expression in maize

Heterosis or hybrid vigor refers to the superior phenotypes of hybrids relative to their parental inbred lines. Recently, soil microbes were identified as an environmental driver of maize heterosis. While manipulation of the soil microbial community consistently altered heterosis, the direction of the effect appeared to be dependent on the microbiome composition, environment, or both. Abiotic factors are well-known modifiers of heterosis expression, however, how the interactive effects between the soil microbial community and abiotic factors contribute to heterosis are poorly understood. To disentangle the proposed mechanisms by which microbes influence heterosis, we characterize the variation in heterosis expression when maize was grown in soil inocula derived from active maize farms or prairies. While we did not observe consistent differences in heterosis among plants grown in these inocula, our observations reaffirm that microbial effects on heterosis are likely specific to the local microbial community. The introduction of a nutrient amendment resulted in greater heterosis expression in the presence of an agricultural inoculum but not a prairie inoculum. We also observed an effect of soil inocula and nutrient treatment on the composition of bacterial and fungal communities in the root endosphere. In addition, the interaction between soil and nutrient treatment significantly affected bacterial community composition, whereas fungal community composition was only marginally affected by this interaction. These results further suggest that the soil microbial community plays a role in maize heterosis expression but that the abiotic environment is likely a larger driver.

plant biology↗
bioRxiv2023

Effective seed sterilization methods require optimization across maize genotypes

Studies of plant-microbe interactions using synthetic microbial communities (SynComs) often require the removal of seed-associated microbes by seed sterilization prior to inoculation to provide gnotobiotic growth conditions. A diversity of seed sterilization protocols have been developed in the past and have been used on different plant species with various amounts of validation. From these studies it has become clear that each plant species requires its own optimized sterilization protocol. It has, however, so far not been tested if the same protocol works equally well for different varieties and seed sources of one plant species. We evaluated six seed sterilization protocols on two different varieties (Sugar Bun & B73) of maize. All unsterilized maize seeds showed fungal growth upon germination on filter paper, highlighting the need for a sterilization protocol. A short sterilization protocol with hypochlorite and ethanol was sufficient to prevent fungal growth on Sugar Bun germinants, however a longer protocol with heat treatment and germination in fungicide was needed to obtain clean B73 germinants. This difference may have arisen from the effect of either genotype or seed source. We then tested the protocol that performed best for B73 on three additional maize genotypes from four sources. Seed germination rates and fungal contamination levels varied widely by genotype and geographic source of seeds. Our study shows that consideration of both variety and seed source is important when optimizing sterilization protocols and highlights the importance of including seed source information in plant-microbe interaction studies that use sterilized seeds.

microbiology↗