Explore the vast range of titles available.

Current methods in measuring maize ( Zea mays L.) southern rust ( Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92–98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

In terms of safe food and a healthy food supply, beans (Phaseolus spp.) are a significant source of protein, carbohydrates, vitamins and minerals especially for poor populations throughout the world. They are also rich in unsaturated fatty acids, such as linoleic and oleic acids. From the past to the present, a large number of breeding studies to increase bean yield, especially the common bean (P. vulgaris L.), have resulted in the registration of many modern varieties, although quality and flavor traits in the modern varieties have been mostly ignored. The aim of the present study, therefore, was to compare protein, fat, fatty acid, and some mineral content such as selenium (Se), zinc (Zn) and iron (Fe) of landraces to modern varieties. The landrace LR05 had higher mineral contents, particularly Se and Zn, and protein than the modern varieties. The landrace LR11 had the highest linoleic acid. The landraces are grown by farmers in small holdings for dual uses, such as both dry seed and snap bean production, and are commercialized with a higher cash price. The landraces of the common bean are, not only treasures that need to be guarded for the future, but also important genetic resources that can be used in bean breeding programs. The results of this study suggest that landraces are essential sources of important nutritional components for food security and a healthy food supply.

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize ( Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68–72%), but inconsistent models. A little sacrifice in accuracy (~60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red–green–blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.

Background Fruit quality traits such as fruit weight, firmness, total soluble solids content, and color strongly influence consumer acceptance, market value, postharvest shelf life, and processing efficiency in tomato. Therefore, these traits are central to breeding programs but remain challenging to dissect due to their polygenic and pleiotropic nature. Results We evaluated 167 accessions from the Varitome collection, encompassing Solanum pimpinellifolium , Solanum lycopersicum var. cerasiforme , and Solanum lycopersicum var. lycopersicum . The Varitome collection represents a rich source of genetic and phenotypic diversity, making it a powerful resource for mapping complex traits. Genome-wide association studies (GWAS) were conducted using the FarmCPU and Blink models, utilizing a dataset of 3,879,252 SNPs, 831,152 INDELs, and 11,447 structural variants (SVs). Six fruit-quality traits were phenotyped: fruit weight, firmness, total soluble solids, lightness ( L* ), chroma ( C* ), and hue ( h° ). Our multi-variant GWAS uncovered both known and novel determinants of fruit quality. Known loci such as PSY1 and fw11.3/CSR were validated, while robust new signals were detected on chromosomes 1, 4, 6, 8, and 12 for °Brix and on chromosome 7 for fruit weight. Several pleiotropic hotspots were identified, particularly on chromosomes 1, 5, 6, and 8 for fruit color, supported by convergent SNP, INDEL, and SV associations. Candidate genes included biosynthetic enzymes ( PSY homologs , LIN5 ), sugar transporters (SWEETs, SUTs, and sugar facilitator proteins), transcriptional regulators (MADS-box, bHLH, TCP, NAC, and MYB families), and genes linked to plastid remodeling, light signaling, and oxidative turnover. Integration of INDELs and SVs across models improved mapping resolution and robustness, enabling the detection of loci that would remain hidden in SNP-only scans. Conclusions This study demonstrates the multilayered genetic networks governing tomato fruit quality and expands the catalog of loci contributing to polygenic traits. By using SNPs, INDELs, and SVs with FarmCPU and Blink models, we provide validated and novel targets for marker-assisted breeding, genomic selection, and functional validation. These findings establish a framework for accelerating the development of tomato cultivars with enhanced fruit weight, sweetness, firmness, and color, thereby supporting both market competitiveness and nutritional quality.

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.

Unoccupied aerial systems (UAS, aka drones) provide high dimensional temporal phenotype data for predictive plant breeding and genetic dissection. Methods to assess temporal phenotype data are an emerging need to predict temporal breeding values of genotypes. Here a novel interaction design was developed and evaluated to include drone flight dates as a component into the mixed model; allowing the temporal changes of drone image derived traits of maize hybrids across different flight dates as well as different management conditions to be monitored. Across 2017 and 2019 respectively, 228 and 100 maize hybrids were grown under two types of management (optimal and late plantings). Seven drone surveys were conducted over each management in 2017 while five drone surveys were conducted over each management in 2019. Temporal plant height (canopy height measurements, CHM) and normalized green‐red difference index (NGRDI) were extracted from each drone survey and used as phenotype data to evaluate the interaction design. Day of flight effects explained the highest amount of total variation for grain yield in the interaction model, meaning the majority of phenotypic variation of CHM and NGRDI occurred across growth with a unique temporal trajectory in each management system. Temporal repeatability values remained higher than 0.5 for CHM and NGRDI in each year. Temporal CHM and NGRDI breeding values of maize hybrids were combined in ridge and lasso regression prediction models. Yield prediction ability of untested genotypes in untested environments were predicted higher by using pedigree × management × flight (PMF) and pedigree× management (PM) interaction results (∼0.34 and 0.52 in 2017 and 2019). Combining environment specific phenomic data (PMF plus PM) gave a larger improvement in yield prediction when the tested and untested environments were less similar. Overall, combined temporal phenomic data could moderately predict grain yield under the most challenging predictive breeding scenario, untested and unrelated genotypes in untested environments. Core Ideas Three‐way interaction model reveals temporal genotypic values and phenotypic plasticity. Phenomic data accurately predicted yield in untested environments and was improved by incorporating three‐way interactions. Maize plant height increased in the later planting faster than in optimal planting until the flowering period.

For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across‐environment sparse genomic prediction models. One phenomic data modality is whole grain near‐infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500‐kernel weight (KW) across 2 years (2011–2012) and two management conditions (water‐stressed and well‐watered) were conducted using combinations of reflectance data obtained from high‐throughput, F2 whole‐kernel scans and genomic data obtained from genotyping‐by‐sequencing within four different cross‐validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024—genomic vs. 0.612 ± 0.045—phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034—genomic vs. 0.617 ± 0.145—phenomic). Multi‐kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single‐kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single‐kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00. Core Ideas Near‐infrared spectroscopy (NIRS) phenomic and genomic prediction had similar accuracies for grain yield and 500‐kernel weight. Models with only genomic/phenomic data often outperformed multi‐kernel models. Lasso regression removed correlated NIRS bands with limited reduction in prediction ability. Plain Language Summary As a common method of identifying high‐performing crop varieties, genomic prediction has been successful in research and commercial crop improvement programs for nearly two decades. However, to determine if a nondestructive method of acquiring spectral data could perform comparably with genomic prediction, near‐infrared spectroscopy (NIRS) of intact maize kernels was conducted in this study using plants grown in multiple environments. Genomic and phenomic prediction were compared for predicting agronomic traits using combinations of genomic‐only, phenomic‐only, and combined prediction models. Though in many cases genomic prediction outperformed NIRS‐based phenomic prediction, these findings indicate that NIRS can still enable comparable prediction ability, highlighting its potential role as a high‐throughput method for predicting economically relevant crop traits. These findings are relevant for breeding programs seeking to screen varieties rapidly and nondestructively.

Flowering time is a critical phenological trait in maize (Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi‐subjective and labor‐intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as unoccupied aerial vehicles or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high‐throughput detection of flowered plots in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here, a methodology for extracting tasseling from UAS red/green/blue imagery using a CNN‐based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas) and then validated through an unrelated set of hybrids. Overall accuracies of 0.946, 0.911, 0.985, and 0.988 were obtained for classifying maize images with or without tassels from College Station, TX, in 2020; College Station, TX, in 2021; Arlington, WI, in 2021; and Madison, WI, in 2021, respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high‐throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments. Plain Language Summary This study focused on using drone images and artificial intelligence (AI) to track when maize plants grow tassels, an important step in their development. Traditional methods rely on people manually observing plants, which is slow, labor‐intensive, and prone to errors. The authors used a drone to take aerial photos and trained a computer model (a convolutional neural network) to recognize maize plots with tassels in these images. The model was tested on data from different locations and years, showing it could accurately detect flowered plots faster than manual methods without sacrificing accuracy. The AI model was also designed to be simple and efficient, so it could run on a laptop or desktop, making it more accessible. This approach could save time and resources in crop breeding and improve how scientists study plants. Future work could apply this to other traits or use higher‐tech sensors for even more data.

Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain. The utility of dense versus sparse temporal collections (e.g., daily, weekly, and monthly flights) has important implications for experimental design, resource allocation, and the scope of scientific questions investigated through UAS. UAS‐derived image data were collected on over 1500 maize hybrid yield trial plots with a temporal (longitudinal, 4D) sampling density of 2.8 days on average between 43 flights throughout the growing season. Correlations of vegetation index (VI) phenomic features between flight dates were generally high between flights separated by only 1 or 2 days but dropped when 3, 4, or more days separated the flights. These varied depending on specific dates and the VI used. Correlations between flights were lower around flowering time than during other parts of the season indicating the phenotypic uniqueness of this developmental period. The cross‐validation accuracy of end of season yields prediction models on untested genotypes from the UAS data (0.59 and 0.62) far exceeded genomic prediction accuracy (0.24) for the same test set hybrids regardless of whether all flight dates were used for prediction or only dates before flowering. Phenomic prediction accuracy marginally increased as additional flight dates were added throughout the season. Core Ideas Temporally dense unoccupied aerial systems (UAS) data contain unique information. Phenomic prediction outperformed genomic prediction. Temporal UAS data improved prediction accuracy.