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Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with -values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum -values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.

High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R 2 ) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.

Pecan scab, caused by the fungal pathogen Venturia effusa , is the most devastating disease of pecan ( Carya illinoinensis ) in the southeastern United States. Resistance to this pathogen is determined by a complex interaction between host genetics and disease pathotype with even field-susceptible cultivars being resistant to most scab isolates. To understand the underlying molecular mechanisms of scab resistance in pecan, we performed a transcriptome analysis of the pecan cultivar, ‘Desirable’, in response to inoculation with a pathogenic and a non-pathogenic scab isolate at three different time points (24, 48, and 96 hrs. post-inoculation). Differential gene expression and gene ontology enrichment analyses showed contrasting gene expression patterns and pathway enrichment in response to the contrasting isolates with varying pathogenicity. The weighted gene co-expression network analysis of differentially expressed genes detected 11 gene modules. Among them, two modules had significant enrichment of genes involved with defense responses. These genes were particularly upregulated in the resistant reaction at the early stage of fungal infection (24 h) compared to the susceptible reaction. Hub genes in these modules were predominantly related to receptor-like protein kinase activity, signal reception, signal transduction, biosynthesis and transport of plant secondary metabolites, and oxidoreductase activity. Results of this study suggest that the early response of pathogen-related signal transduction and development of cellular barriers against the invading fungus are likely defense mechanisms employed by pecan cultivars against non-virulent scab isolates. The transcriptomic data generated here provide the foundation for identifying candidate resistance genes in pecan against V . effusa and for exploring the molecular mechanisms of disease resistance.

Background The Southern root-knot nematode ( Meloidogyne incognita ) poses a substantial threat to cotton ( Gossypium hirsutum L.) by causing significant agricultural losses. Host plant resistance is the most plausible approach for minimizing these losses. QTL mapping and early transcriptomic studies have identified candidate genes within the QTL regions on chromosome 11 ( qMi-C11 ) and chromosome 14 ( qMi-C14 ). Although these QTL regions have been fine-mapped and candidate genes identified, expression profiling of Meloidogyne-Gossypium interactions across different stages of infection could further refine the list of candidate genes. This study advances our understanding of the molecular mechanisms underlying the resistance conferred by qMi-C11 and qMi-C14 against Southern root-knot nematode. Results Using time-course RNA-seq analyses across nematode developmental phases, we uncovered transcriptomic events—both genome-wide and within QTL intervals—underlying defense responses during compatible interactions (with Cocker 201, a susceptible line) and incompatible interactions (with M-120 RNR, a resistant line). Basal defense responses were observed in both compatible and incompatible interactions, with stronger expression in the incompatible interaction. Nematode-responsive genes associated with defense pathways showed distinct dynamics, characterized by repression during compatible interactions and early induction, greater diversity, and heightened upregulation during incompatible interactions. This study uncovers a broad repertoire of disease resistance and putative resistance genes, as well as pathogenesis-related genes, ligands, and receptors, that are differentially expressed in response to nematode parasitism. Mapping of these genes across the cotton genome identified promising candidates, including Gh_A11G3090 ( PUB21 ) and Gh_A11G2836 ( RPPL1 ) within the chromosome 11 QTL region, andGh_D02G0257 ( RLP12 ) and Gh_D02G0259 ( RLP12 ) within the chromosome 14 QTL region. Conclusions The findings of this study deepen our understanding of host-nematode interactions, identify candidate genes for downstream applications, and contribute to advancements in resistance breeding and sustainable nematode management strategies.

Host plant resistance is the most practical approach to control the Southern root-knot nematode (Meloidogyne incognita; RKN), which has emerged as one of the most serious economic pests of Upland cotton (Gossypium hirsutum L.). Previous QTL analyses have identified a resistance locus on chromosome 11 (qMi-C11) affecting galling and another locus on chromosome-14 (qMi-C14) affecting egg production. Although these two QTL regions were fine mapped and candidate genes identified, expression profiling of genes would assist in further narrowing the list of candidate genes in the QTL regions. We applied the comparative transcriptomic approach to compare expression profiles of genes between RKN susceptible and resistance genotypes at an early stage of RKN development that coincides with the establishment of a feeding site and at the late stage of RKN development that coincides with RKN egg production. Sequencing of cDNA libraries produced over 315 million reads of which 240 million reads (76%) were mapped on to the Gossypium hirsutum genome. A total of 3,789 differentially expressed genes (DEGs) were identified which were further grouped into four clusters based on their expression profiles. A large number of DEGs were found to be down regulated in the susceptible genotype at the late stage of RKN development whereas several genes were up regulated in the resistant genotype. Key enriched categories included transcription factor activity, defense response, response to phyto-hormones, cell wall organization, and protein serine/threonine kinase activity. Our results also show that the DEGs in the resistant genotype at qMi-C11 and qMi-C14 loci displayed higher expression of defense response, detoxification and callose deposition genes, than the DEGs in the susceptible genotype.

Upland cotton ( Gossypium hirsutum ) faces the challenge of limited genetic diversity in the elite or improved gene pool. To address this issue, we explored alleles contributed by five ‘converted’ exotic lines sampling most of the undomesticated botanical races of G. hirsutum , in BC 1 F 2 and F 3 populations. Joint analysis of all populations along with population-specific analyses identified 38 unique QTL for six different fiber quality traits. At 15 of these loci, DES56 or the elite allele improved upon all the exotics. For another 15, only a single of the five exotics improved upon the elite allele, suggesting the rare alleles that may not have been sampled in the cotton domestication or improvement. At the remaining 8 QTL, multiple exotic lines contributed the superior allele, suggesting that DES56 (and by extension the elite gene pool) has chronically poor alleles at these loci. Converted strains T1046, T326, and T063 showed the highest potential for contributions to cotton fiber quality breeding programs. Upper Half Mean Length and Fiber Strength showed multiple QTL regions affecting both traits simultaneously, while the Uniformity Index showed the smallest heritability values. The estimation of pairwise genetic distances for six parental lines indicates that DES56 has a higher genetic similarity with each exotic line than the exotic lines have with each other. Most of the detected QTL were ‘minor’ (explaining less than 10% of variance) supporting the implementation of genomic selection techniques to utilize the cumulative effects of most of these QTL distributed genome-wide. Finally, some regions were consistently unfavorable for exotic introgression such as on chromosomes A13 and D09, indicating the possible genome-wide haplotypes that may combine the benefits of a history of scientific breeding of the elite gene pool.

Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests—thus supporting the development of effective control strategies and facilitating genetic breeding research—we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests.

The reniform nematode ( Rotylenchulus reniformis Linford & Oliveira) is a serious pathogen of Upland cotton ( Gossypium hirsutum L.) wherever it is grown throughout the United States. Upland cotton resistance to R . reniformis derived from the G . barbadense accession GB713 is largely controlled by the Ren barb2 locus on chromosome 21. Ren barb2 has proven useful as a tool to mitigate annual cotton yield losses due to R . reniformis infection; however, very little is known about the molecular aspects of Ren barb2 -mediated resistance and the gene expression changes that occur in resistant plants during the course of R . reniformis infection. In this study, two nearly isogenic lines (NILs), with and without the Ren barb2 locus, were inoculated with R . reniformis and RNAs extracted and sequenced from infected and control roots at 5-, 9-, and 13-dai (days after inoculation). A total of 966 differentially expressed genes (DEGs) were identified in the resistant NIL while 133 DEGs were discovered from the susceptible NIL. In resistant plants, biological processes related to oxidation-reduction reactions and redox homeostasis were enriched at each timepoint with such genes being up-regulated at 5- and 9-dai but then being down-regulated at 13-dai. DEGs associated with cell wall reinforcement and defense responses were also up-regulated at early timepoints in resistant roots. In contrast, in susceptible roots, defense-related gene induction was only present at 5-dai and was comprised of far fewer genes than in the resistant line. ERF, WRKY, and NAC transcription factor DEGs were greatly enriched at 13-dai in resistant roots but were absent in the susceptible. Cluster analysis of resistant and susceptible DEGs revealed an ‘early’ and ‘late’ response in resistant roots that was not present in the susceptible NIL. SNP analysis of transcripts within the Ren barb2 QTL interval identified five genes having non-synonymous mutations shared by other Ren barb2 germplasm lines. The basal expression of a single candidate gene, Gohir.D11G302300, a CC-NBS-LRR homolog, was ~3.5-fold greater in resistant roots versus susceptible. These data help us to understand the Ren barb2 -mediated resistance response and provides a short list of candidate resistance genes that potentially mediate that resistance.

In this study, we investigated the potential involvement of endogenous viral elements (EVEs) in the development of apical tissue necrosis, resulting in the terminal abortion of upland cotton (Gossypium hirsutum L.) in Georgia. The high-throughput sequence analysis of symptomatic and asymptomatic plant tissue samples revealed near-complete EVE-Georgia (EVE-GA) sequences closely related to caulimoviruses. The analysis of EVE-GA’s putative open reading frames (ORFs) compared to cotton virus A and endogenous cotton pararetroviral elements (eCPRVE) revealed their similarity in putative ORFs 1–4. However, in the ORF 5 and ORF 6 encoding putative coat protein and reverse transcriptase, respectively, the sequences from EVE-GA have stop codons similar to eCPRVE sequences from Mississippi. In silico mining of the cotton genome database using EVE-GA as a query uncovered near-complete viral sequence insertions in the genomes of G. hirsutum species (~7 kb) but partial in G. tomentosum (~5.3 kb) and G. mustelinum (~5.1 kb) species. Furthermore, cotton EVEs’ episomal forms and messenger RNA (mRNA) transcripts were detected in both symptomatic and asymptomatic plants collected from cotton fields. No significant yield difference was observed between symptomatic and asymptomatic plants of the two varieties evaluated in the experimental plot. Additionally, EVEs were also detected in cotton seeds and seedlings. This study emphasizes the need for future research on EVE sequences, their coding capacity, and any potential role in host immunity or pathogenicity.

Pearl millet [ Cenchrus americanus (L.) Morrone, formerly Pennisetum glaucum (L.) R. Br.] is the sixth most important cereal globally and is used for forage and feed in the U.S. To identify genomic regions governing important physiological, agronomic and yield related traits, a recombinant inbred line population derived from the cross between Tift 99D 2 B 1 × Tift 454 was phenotyped in the field in 2006, 2007 and 2013. In addition, the population was phenotyped for root-knot nematode resistance in the greenhouse during 2009. Using a previously generated genetic map containing 505 single nucleotide polymorphism markers and composite interval mapping, we identified 45 QTLs for eight traits (plant height, stem diameter, days to heading, panicle diameter, panicle length, 1000 seed weight, Pyricularia leaf spot disease, and root-knot nematode egg mass) across almost all linkage groups. These QTLs explained 6.31 to 32.51% of phenotypic variance for each trait and were consistently detected over different environments. Plant height and days to heading were colocalized on LG2 and LG5 showing maturity and plant height are linked and influence each other, similarly to other cereal crops. Interestingly, 5 of 19 QTLs linked to plant height, stem diameter, panicle diameter, and panicle length colocalized to the same locations on LG3, indicating breeding for one trait could simultaneously improve the other. The markers and genes identified in the present study can be used in developing high yielding pearl millet varieties using marker-assisted selection.