Selection Of Genotypes Research Articles

Genetic diversity study of bacterial wilt-tolerant bell pepper (Capsicum annuum) genotypes using cluster and principal component analysis

The present study was carried out during summer and rainy (kharif) seasons of 2019 and 2020 at the research farm of Himachal Pradesh Krishi Vishvavidyalaya, Palampur, Himachal Pradesh to study the genetic diversity among 43 bacterial wilt tolerant genotypes of bell pepper (Capsicum annuum L. var. grossum Sendt), focusing on 12 quantitative and 3 qualitative traits along with disease resistance. The experiment was laid out in a randomized complete block design (RCBD) with 3 replications. Following hierarchical cluster analysis, the 43 unique genotypes were categorized into 5 separate clusters. Cluster I consisted of maximum number of genotypes (39), followed by clusters II, III, IV and V, each having only one genotype. The greatest inter-cluster distance was observed between cluster II and V (3090.70), while the smallest distance was noted between cluster II and III (1299.21). Cluster I displayed the greatest intra-cluster distance (803.75), while clusters II, III, IV and V showed the lowest distances (0.00). The primary contributors to genetic divergence were found to be capsanthin content (23.4%), followed by ascorbic acid (21.4%), fruit length (12.5%), plant survival (11.1%) and marketable fruit yield/plant (10.9%). Principal component analysis indicated that the initial six principal components, each possessing an eigen value surpassing one, jointly accounted for 70.62% of the total observed variability. PCA biplot revealed that, the traits namely, marketable fruits/plant, average fruit weight, harvest duration and plant height were the major yield contributing traits. The results underscored the importance of genetic characterization and the selection of diverse genotypes for hybridization, facilitating the development of improved bell pepper cultivars with enhanced productivity and quality.

Evaluation of tuber quality, yield and yield related traits of potato (Solanum tuberosum L.) genotypes at Holetta, Central Ethiopia

In Ethiopia, potato is a food security and income generating crop for millions of smallholder farmers. Previous research efforts related to variety development was focusing on tube yield and late blight resistance with wide adaptability. So far, the potato research program in the country however, devoted less on tuber quality traits and processing parameters except few recently released varieties that considered tuber quality traits for processing. These few potato varieties with processing qualities not yet satisfied the ever-emerging processing industries for chips and French fries in the country. Hence, this study aimed to seek evaluating more potato genotypes that have merits for tuber quality traits used for processing and yield attributes. The experiment was laid out in a randomized completed block design with three replications using 24 potato genotypes to evaluate the processing quality traits of advanced potato genotypes at Holetta Agricultural Research Centre in 2017 main cropping season. The results of the analysis of variance revealed that all the traits showed significant differences among the genotypes indicating there is wide genetic variation. The highest and significantly different tuber physical quality traits of geometric mean diameter and surface area was recorded from CIP395017.229, sphericity of the tuber from CIP396027.205, and length to width ratio from CIP399075.7. The specific gravity of tubers, dry matter content and total starch content also ranged from 1.070 to 1.103 gcm−3, 18.67 to 25.75%, and 12.64% to 18.95%, respectively. The five advanced genotypes CIP398098.65, CIP392617.54, CIP398190.404, CIP394611.112, and C398190.89 were selected for their high total tuber yields over 36.80 t/ha, with more than 31.47 t/ha marketable tuber yield, greeter than 69.92 g/tuber average tuber weight, higher than 58.41 mm3 geometric mean diameter, more than 21.25% dry matter content, excellent tuber characteristics and processing quality traits for chips and French fries making. From this study, selection of potato genotypes that consider both external and internal tuber quality traits would expedite the process of advancing ample potato varieties with processing qualities in the country considering comprehensive study by including more potato genotypes across wider growing environments.

Genotype-specific responses to in vitro drought stress in myrtle (Myrtus communis L.): integrating machine learning techniques.

Myrtle (Myrtus communis L.), native to the Mediterranean region of Türkiye, is a valuable plant with applications in traditional medicine, pharmaceuticals, and culinary practices. Understanding how myrtle responds to water stress is essential for sustainable cultivation as climate change exacerbates drought conditions. This study investigated the performance of selected myrtle genotypes under in vitro drought stress by employing tissue culture techniques, rooting trials, and acclimatization processes. Genotypes were tested under varying polyethylene glycol (PEG) concentrations (1%, 2%, 4%, and 6%). Machine learning (ML) algorithms, including Gaussian process (GP), support vector machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were utilized to model and predict micropropagation and rooting efficiency. The research revealed a genotype-dependent response to drought stress. Black-fruited genotypes exhibited higher micropropagation rates compared to white-fruited ones under stress conditions. The application of ML models successfully predicted micropropagation and rooting efficiency, providing insights into genotype performance. The findings suggest that selecting drought-tolerant genotypes is crucial for enhancing myrtle cultivation. The results underscore the importance of genotype selection and optimization of cultivation practices to address climate change impacts. Future research should explore the molecular mechanisms of stress responses to refine breeding strategies and improve resilience in myrtle and similar economically important crops.

UAV-Based Phenotyping: A Non-Destructive Approach to Studying Wheat Growth Patterns for Crop Improvement and Breeding Programs

Rising food demands require new techniques to achieve higher genetic gains for crop production, especially in regions where climate can negatively affect agriculture. Wheat is a staple crop that often encounters this challenge, and ideotype breeding with optimized canopy traits for grain yield, such as determinate tillering, synchronized flowering, and stay-green (SG), can potentially improve yield under terminal drought conditions. Among these traits, SG has emerged as a key factor for improving grain quality and yield by prolonging photosynthetic activity during reproductive stages. This study aims to highlight the importance of growth dynamics in a wheat mapping population by using multispectral images obtained from uncrewed aerial vehicles as a high-throughput phenotyping technique to assess the effectiveness of using such images for determining correlations between vegetation indices and grain yield, particularly regarding the SG trait. Results show that the determinate group exhibited a positive correlation between NDVI and grain yield, indicating the effectiveness of these traits in yield improvement. In contrast, the indeterminate group, characterized by excessive vegetative growth, showed no significant NDVI–grain yield relationship, suggesting that NDVI values in this group were influenced by sterile tillers rather than contributing to yield. These findings provide valuable insights for crop breeders by offering a non-destructive approach to enhancing genetic gains through the improved selection of resilient wheat genotypes.

Influence of β-lactoglobulin and κ-casein genotype on whey protein denaturation in heated bovine milk and rennet whey

Whey proteins provide nutritional and functional properties in many dairy products, and their denaturation induced by heat has drawn attention in dairy research and industry. Denatured whey proteins have functional properties that are beneficial for certain applications, such as yogurt, while uncontrolled denatured protein aggregation can impair certain processes, such as rennet coagulation. The possible effect of genetic polymorphism of β-lg and κ-CN on heat-induced denaturation of whey proteins in milk and in rennet whey was investigated in this study. No effect of κ-CN genotype on the denaturation of β-lg and α-lac was detected, while the A variant of β-lg was more heat-stable than the B variant. More denatured β-lg B was shown to interact with casein micelles to form insoluble aggregates compared to the A variant. These results may provide guidance for selection of specific milk genotypes for applications where whey protein denaturation needs to be minimised or else preferred, such as foaming and emulsification.

From landraces to haplotypes, exploiting a genomic and phenomic approach to identify heat tolerant genotypes within durum wheat landraces

Dry and hot climates severely impact wheat yields, necessitating the development of innovative solutions to accelerate the breeding and selection of more adaptable durum wheat genotypes. The aim of this study was to identify new wheat ecotypes that can bridge the gap between commercial varieties and adaptability to ongoing climate change. In this study, advanced genomic and phenomic techniques were combined to characterize a set of durum wheat landraces derived from single seed descent (SSD). This approach enabled the identification of novel variability in the TdHsp26-A1 and -B1 genes. As a result, 38 durum wheat genotypes were analyzed using targeted enrichment PCR, leading to the identification of 17 novel haplotype combinations with SNPs in the TdHsp26 genes. The response of these SSD haplotypes to heat stress was characterized at both the seedling and tillering growth stages. Phenotypic analysis of contrasting genotypes led to the selection of two distinct genotypes: SSD69 and SSD397. During heat stress, SSD69 exhibited altered accumulation of H2O2 and MDA content under both growth conditions, providing new insights into the oxidative response to heat stress. Additionally, this work identifies phenotypic traits that are suitable for detecting differences between variants. The geographic distribution of the different alleles aligned with the spread of durum wheat from its center of origin.

Potential of the Colombian Coffee Collection as a Source of Genetic Resistance to Colletotrichum kahawae JM Waller PD Bridge

Coffee berry disease (CBD) is not present in the Americas and presents a potential risk for growing coffee. Therefore, Colombia, which has been in scientific cooperation with the Centro de Investigação de Ferrugens do Cafeeiro (CIFC) of Portugal for more than 30 years, has been evaluating the genetic resistance of nine populations of C. arabica to 13 isolates of Colletotrichum kahawae JM Waller and PD Bridge, which are diverse in terms of aggressiveness and geographical origin. The phenotypes observed in the interaction between C. arabica and C. kahawae were used to develop a statistically reliable scale (p-value ≥ 0.001) to categorize resistance in C. arabica into five classes, and this scale was used to classify the nine populations of C. arabica evaluated. The results allowed us to corroborate the potential of Timor Hybrid CIFC 1343 (TH CIFC 1343) as a source of genetic resistance to CBD and to identify new genetic sources not yet explored for the development of varieties in Colombia that may eventually mitigate the effects of CBD in the face of increasing rainfall events and minimum temperatures due to climate change, which can favor disease development. Additionally, the results suggest that the existence of races in the C. arabica–C. kahawae complex is probable, and a selection of genotypes was identified as a possible differential series of races in C. kahawae.

Chlorophyll Fluorescence in Wheat Breeding for Heat and Drought Tolerance

The constantly growing need to increase the production of agricultural products in changing climatic conditions makes it necessary to accelerate the development of new cultivars that meet the modern demands of agronomists. Currently, the breeding process includes the stages of genotyping and phenotyping to optimize the selection of promising genotypes. One of the most popular phenotypic methods is the pulse-amplitude modulated (PAM) fluorometry, due to its non-invasiveness and high information content. In this review, we focused on the opportunities of using chlorophyll fluorescence (ChlF) parameters recorded using PAM fluorometry to assess the state of plants in drought and heat stress conditions and predict the economically significant traits of wheat, as one of the most important agricultural crops, and also analyzed the relationship between the ChlF parameters and genetic markers.

Selection of biomass sorghum genotypes based on multi-environment trials and multiple traits for 2G ethanol purpose

Selection of biomass sorghum genotypes based on multi-environment trials and multiple traits for 2G ethanol purpose

Comparative analysis of deep learning and machine learning-based models for simultaneous prediction of minerals in perilla (Perilla frutescens L.) seeds using near-infrared reflectance spectroscopy

Perilla seeds contain a rich array of essential minerals, thus having the potential to address multiple micronutrient deficiencies at a time. However, traditional methods of mineral estimation are complex, time-consuming, expensive, and require technical expertise. This study includes the development of Near-Infrared Reflectance Spectroscopy (NIRS)-based prediction models for predicting five important minerals (Calcium, Copper, Magnesium, Manganese, and Phosphorus) using machine learning and deep learning techniques. Four models, including 1D Convolutional Neural Networks (1D CNNs), Artificial Neural Networks (ANNs), Random Forests (RFs), and Support Vector Regression (SVR), were developed and evaluated. The developed 1D CNN model outperformed other considered models in predicting calcium, magnesium, and phosphorus content with RPD (Residual Prediction Deviation) values of 1.75, 1.83, and 2.96, respectively. Whereas, SVR performed best in predicting copper and manganese with an RPD of 1.82 and 2.2, respectively. The 1D CNN model demonstrated R2 (Coefficient of determination) values above 0.65 for all minerals, with a maximum of 0.88 for phosphorus. In addition, the developed models performed superior as compared to the Partial Least Square Regression method (R2= 0.32). The developed models provide efficient tools for rapidly screening perilla germplasm available in global repositories, thus aiding in the selection of mineral-rich genotypes to mitigate micronutrient deficiencies.

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.

Effects of salinity and nutrient stress on a toxic freshwater cyanobacterial community and its associated microbiome: An experimental study.

We aimed to evaluate the ability of naturally occurring colonies of Microcystis, embedded in a thick mucilage, to persist in estuarine waters. In two batch experiments, we examined the dynamics of microbial communities, including cyanobacteria and associated heterotrophic bacteria, sampled from the field during both a cyanobacterial bloom (non-limiting nutrient condition) and the post-bloom period (limiting nutrient condition), and subjected them to a salinity gradient representative of the freshwater-marine continuum. We demonstrated that both Microcystis aeruginosa and M. wesenbergii survived high salinities due to osmolyte accumulation. Specifically, prolonged exposure to high salinity led to betaine accumulation in the cyanobacterial biomass. The relative abundance of the mcyB gene remained around 30%, suggesting no selection for toxic genotypes with salinity or nutrient changes. Microcystins were predominantly intracellular, except at high salinity levels (>15), where more than 50% of the total microcystin concentration was extracellular. In both nutrient conditions, over 70% of the heterotrophic bacterial community belonged to the Gammaproteobacteria family, followed by the Bacteroidota. Bacterial community composition differed in both size fractions, as well as along the salinity gradient over time. Finally, genus-specific core microbiomes were identified and conserved even under highly stressful conditions, suggesting interactions that support community stability and resilience.

Selection of Hull-Less Barley Superior Genotypes for the Warm Climate of Southern Fars in Iran

Selection of Hull-Less Barley Superior Genotypes for the Warm Climate of Southern Fars in Iran

Assessing the genetic diversity and character association of yield components in litchi (litchi chinensis sonn.)

Eighteen litchi genotypes were evaluated in this study for genetic advancement, heritability, variability, and association studies. The genotype ‘CHL-4’ exhibited maximum fruit weight (28.12 g), aril weight (14.00 g), aril thickness (9.65 mm) and seed weight (4.86 g). Fruit length (10.70 and 10.46%), panicle width (10.61 and 10.37%), and panicle length (15.75 and 15.45%) all had substantial phenotypic and genotypic coefficients of variation. All the metrics, with the exception of fruit diameter, aril thickness, seed weight, and number of fruits/panicle, showed higher heritability estimations with substantial genetic advances. In addition to being negatively correlated with fruit weight, aril weight, seed weight and pulp thickness, yield also showed a strong positive correlation with the number of fruits/panicle and aril percentage. Path analysis made the impact of different components on yield very evident. The maximum positive direct effect on yield at genotypic level was through fruit weight (10.120) followed by seed weight (4.070), panicle length (1.550), panicle girth (1.170) and negative direct effect via aril weight (-0.12.860) followed by panicle width (-11.830), fruit length (-6.290). In order to select a diverse parent for a subsequent breeding effort, cluster analysis assists in classifying comparable objects into appropriate categories. Therefore, the goal of the study was to confirm the genetic variability across genotypes of litchi and identify prospective features for the selection of genotypes that are superior. Bangladesh J. Bot. 53(3): 465-473, 2024 (September)

Influence of different genotype combinations of β-lactoglobulin and β-casein in cow milk on physicochemical and sensory properties of stirred yoghurt

In this study, stirred yoghurts were produced from Turkish Holstein cow milk with different genotype combinations of β-lactoglobulin and β-casein. The distribution of AA, AB, and BB genotypes of β-lactoglobulin and A1A1, A1A2, and A2A2 genotypes of β-casein was determined in a total of 135 cow milk samples. Raw cow milk samples containing AA genotype of β-LG exhibited higher non-fat total solids, protein, and fat contents in comparison to milk containing AB and BB genotypes of β-LG. Compared to the samples made from milk with other β-LG genotypes, the stirred yoghurt made from milk with β-LG AA genotype showed lower degree of syneresis and higher for hardness, apparent viscosity, and consistency coefficient. The lowest number of grains, the perimeter of grains, and the visual roughness values were obtained in the stirred yoghurts made from milk with BB genotype of β-LG and A2A2 genotype of β-CN. The sensory analysis revealed that β-CN genotypes of milk significantly influenced the overall acceptability scores of the stirred yoghurts, while β-LG genotypes did not significantly influence these scores except for the visual grainy score. The findings of this study showed that the selection of specific milk protein genotypes resulted in substantial differences in the physicochemical and sensory properties of stirred yoghurt.

Use of KASP technologies for the identification of alleles of polyphenoloxidase genes of common wheat (Triticum aestivum L.)

Aim. Characterization of recombinant inbred lines F7 Luzanivka odeska/Odeska chervonokolosa by polyphenol oxidase (PPO) genes using KASP technologies and detection of the associations of allelic differences in Ppo genes with agronomically important traits of common wheat. Methods. Competitive allele-specific PCR (KASP). Phenological observations, elements of yield structure. Results. The polymorphism of the parental genotypes and the population of recombinant inbred lines for Ppo-A1 and Ppo-D1 genes was evaluated. A polymorphism between parental forms on the Ppo-D1 gene was detected. A comparison of the data of KASP-analysis of lines with the results of evaluation of lines by eight agronomic traits of wheat was carried out. Conclusions. Differences in the alleles of the Ppo-D1 gene did not affect the traits duration of a period prior to heading, plant height, productive tillering, grain number per spike, thousand-grain weight, productive tiller number per unit area and grain yield. An increase in the grain weight per spike was observed in lines with a favorable Ppo-D1a allele. The selection of genotypes with the Ppo-D1a allele, which corresponds to the low activity of the PPO enzyme, does not have a negative effect on the agronomically important characteristics of wheat.

Heterotic Studies for Yield and Yield Attributing Characters in Finger Millet (Eleusine coracana L. Gaertn)

In the present investigation twelve crosses were generated by crossing three lines with four testers in Line × Tester mating design during kharif 2023 at Agricultural Research Station, Perumallapalle, Tirupati. Twelve crosses along with seven parents and check (Tirumala) were evaluated during rabi 2023-24 in Randomised Block Design with two replications at Agricultural Research Station, Perumallapalle, Tirupati. ANOVA revealed significant difference among genotypes for all characters studied. Heterosis studies helps in finding out superior hybrids over mid parent (Average heterosis), better parent (heterobeltiosis) and standard check (economic heterosis). Among the crosses, PPR1216 × VL 400 was identified as superior cross as it recorded high magnitude of per se performance and all three types of heterosis for days to 50% flowering, number of productive tillers per plant, number of fingers per ear and grain yield per plant. The heterosis will be exploited through selection of superior and character specific genotypes in the segregating generations for the evolution of high yielding varieties. Statistical analysis was carried out by using OP STAT and R Software.

Identification of Key Traits for Yield Improvement in Brinjal Through Correlation and Path Coefficient Analysis

Brinjal (Solanum melongena L.), also known as poor man’s vegetable, is widely distributed and grown in almost all parts of India. The assessment of genetic variation, correlation and path analysis for various yield and yield attributing traits in the available germplasm is crucial for selecting superior genotypes with desired traits such as yield, fruit quality, disease resistance, and adaptability to environmental conditions, thereby enhancing the potential for further crop improvement through breeding. Therefore, the present study was conducted to assess the phenotypic correlation and path coefficient among various yield and yield related traits for 42 genotypes of brinjal at the Horticulture Research Centre, SVPUA&T, Meerut (U.P.), India, over two consecutive Kharif seasons in 2022 and 2023 in Randomized Complete Block Design with three replications. The analysis of variance indicated significant variability across all the traits examined. In general, magnitudes of genotypic correlation coefficient were higher than their corresponding phenotypic correlation coefficient, implying a significant inherent relationship in different pair of traits. Based on the correlation coefficient analysis, fruit yield per plant had significant and positive association with fruit yield per plot, fruit yield per hectare, fruit weight, number of fruits per plant, fruit length and fruit girth both at genotypic and phenotypic levels. Path coefficient analysis showed that the traits viz., number of fruits per plant, fruit weight, fruit girth and fruit length exhibited highest direct effects on fruit yield and also indirectly influenced by the other yield contributing traits of brinjal. It is concluded from the study that these traits may be used as key indices towards the direct selection of elite genotypes for the successful breeding programme for yield improvement of brinjal germplasm.

Vertical farming goes dynamic: optimizing resource use efficiency, product quality, and energy costs

Vertical farming is considered to be a key enabler for transforming agrifood systems, especially in or nearby urbanized areas. Vertical farming systems (VFS) are advanced indoor cropping systems that allow for highly intensified and standardized plant production. The close control of environmental parameters makes crop production stable and repeatable, ensuring year-round uniform product quality and quantity irrespective of location. However, due to continuous changes in plant physiology and development, as well as frequent changes in electricity prices, the optimum conditions for crop production and its associated costs can change within days or even minutes. This makes it beneficial to dynamically adjust setpoints for light (intensity, spectrum, pattern, and daylength), CO2, temperature, humidity, air flow, and water and nutrient availability. In this review, we highlight the beneficial effects that dynamic growth conditions can have on key plant processes, including improvements in photosynthetic gas exchange, transpiration, organ growth, development, light interception, flowering, and product quality. Our novel findings based on modeling and experimentation demonstrate that a dynamic daily light intensity pattern that responds to frequent changes in electricity prices can save costs without reducing biomass. Further, we argue that a smart, dynamic VFS climate management requires feedback mechanisms: several mobile and immobile sensors could work in combination to continuously monitor the crop, generating data that feeds into crop growth models, which, in turn, generate climate setpoints. In addition, we posit that breeding for the VFS environment is at a very early stage and highlight traits for breeding for this specialized environment. We envision a continuous feedback loop between dynamic crop management, crop monitoring, and trait selection for genotypes that are specialized for these conditions.

Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis.

Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhancing scion physiology. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) have been identified as ideal targets for potential influence by rootstock and breeding. However, leaf specific direct measurement of these photosynthetic parameters is time consuming, limiting the information scope and the number of individuals that can be screened. This study aims to overcome these limitations by employing hyperspectral imaging combined with artificial intelligence (AI) to predict these key photosynthetic traits at the canopy level. Hyperspectral imaging captures detailed optical properties across a broad range of wavelengths (400 to 1000 nm), enabling use of all wavelengths in a comprehensive analysis of the entire vine's photosynthetic performance (Vcmax and Jmax). Artificial intelligence-based prediction models that blend the strength of deep learning and machine learning were developed using two growing seasons data measured post-solstice at 15 h, 14 h, 13 h and 12 h daylengths for Vitis hybrid 'Marquette' grafted to five commercial rootstocks and 'Marquette' grafted to 'Marquette'. Significant differences in photosynthetic efficiency (Vcmax and Jmax) were noted for both direct and indirect measurements for the six rootstocks, indicating that rootstock genotype and daylength have a significant influence on scion photosynthesis. Evaluation of multiple feature-extraction algorithms indicated the proposed Vitis base model incorporating a 1D-Convolutional neural Network (CNN) had the best prediction performance with a R2 of 0.60 for Vcmax and Jmax. Inclusion of weather and chlorophyll parameters slightly improved model performance for both photosynthetic parameters. Integrating AI with hyperspectral remote phenotyping provides potential for high-throughput whole vine assessment of photosynthetic performance and selection of rootstock genotypes that confer improved photosynthetic performance potential in the scion.