Precision Phenotyping in Crop Science: From Plant Traits to Gene Discovery for Climate‐Smart Agriculture

Plant Breeding, Год журнала: 2024, Номер unknown

Опубликована: Окт. 20, 2024

ABSTRACT The global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding bolstered with efficient nondestructive trait documentation approaches vital. High‐throughput phenotyping (HTCP) platforms have prominently emerged as mainstream approach for reducing the bottleneck in programmes. HTCP has potential to provide detailed quantitative information large populations under different growth stages across diverse environmental regimes, facilitating accelerated strategies. New imaging also enable characterization wide range above below‐ground parameters. specificity use sensors, automation data collection, large‐scale handling systems accurate analytical tools substantial role dynamic monitoring big interpretation. are capable making measurements physiological, morphological, biochemical stress responses plants. Developments sensors improved precision, intervention unmanned aerial vehicles, robotics, computed tomography machine learning given dramatic developmental leap phenotyping. This review provides an avenue understanding various high‐throughput platforms, working principles, current developments contributions crops laboratory field conditions. A comparative idea advantages pitfalls these available help researchers choosing right technology suiting specific practical requirements. Furthermore, aims novel future prospects requirements that potentially widen application utilization technologies agriculture.

Язык: Английский

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Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change

Plants, Год журнала: 2025, Номер 14(6), С. 907 - 907

Опубликована: Март 14, 2025

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses environmental offering new opportunities for both stress resilience breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, machine learning, enhance our ability assess traits under various including drought, salinity, extreme temperatures, pest disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency programs. HTP can also play vital role by accelerating genetic gain through precise trait evaluation hybridization enhancement. However, challenges data standardization, management, high costs equipment, complexity linking phenotypic observations improvements limit its broader application. Additionally, variability genotype-by-environment interactions complicate reliable selection. Despite these challenges, advancements in robotics, artificial intelligence, automation are precision scalability analyses. This review critically examines dual assessment tolerance performance, highlighting transformative potential existing limitations. By addressing key leveraging technological advancements, significantly research, discovery, parental selection, scheme optimization. While current methodologies still face constraints fully translating insights into practical applications, continuous innovation high-throughput holds promise revolutionizing ensuring sustainable agricultural production changing climate.

Язык: Английский

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An automated root phenotype platform enables nondestructive high-throughput root system architecture dissection in wheat

PLANT PHYSIOLOGY, Год журнала: 2025, Номер 198(1)

Опубликована: Апрель 12, 2025

The root system architecture (RSA) determines plant growth and yield. characterization of optimal RSA discovery genetic loci or candidate genes that control traits are therefore important research goals. However, the hidden nature makes it difficult to perform nondestructive, rapid analyses RSA. In this study, we developed an automated, high-throughput phenotyping platform (Root-HTP) a corresponding data processing pipeline for efficient, large-scale wheat (Triticum aestivum L.) This is capable tracking dynamics variation across all developmental stages. situ using Root-HTP extracted 47 traits, including 33 novel in 23 other crops. We used trait from yield conduct genome-wide association study (GWAS) 155 accessions, which identified 2,650 SNPs 233 quantitative (QTLs) associated with aspects architecture. gene TaMYB93 was detected QTL tortuosity, EMS mutants confirmed its effect on wheat. explored relationship between root- yield-related 20 root-related QTLs were also traits. Furthermore, have built predictive model based 18 propose parsimonious ideotype high yields. generated provide insight into support ideotype-based breeding prediction.

Язык: Английский

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A Review of Artificial Intelligence Techniques for Wheat Crop Monitoring and Management

Agronomy, Год журнала: 2025, Номер 15(5), С. 1157 - 1157

Опубликована: Май 9, 2025

Artificial intelligence (AI) techniques, particularly machine learning and deep learning, have shown great promise in advancing wheat crop monitoring management. However, the application of AI this domain faces persistent challenges that hinder its full potential. Key limitations include high variability agricultural environments, which complicates data acquisition model generalization; scarcity limited diversity labeled datasets; substantial computational demands associated with training deploying models. Additionally, difficulties ground-truth generation, cloud contamination remote sensing imagery, coarse spatial resolution, “black-box” nature models pose significant barriers. Although strategies such as augmentation, semi-supervised crowdsourcing been explored, they are often insufficient to fully overcome these obstacles. This review provides a comprehensive synthesis recent advancements for applications, critically examines major unresolved challenges, highlights promising directions future research aimed at bridging gap between academic development real-world practices.

Язык: Английский

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Fulvic Acid Enhances Oat Growth and Grain Yield Under Drought Deficit by Regulating Ascorbate–Glutathione Cycle, Chlorophyll Synthesis, and Carbon–Assimilation Ability

Agronomy, Год журнала: 2025, Номер 15(5), С. 1153 - 1153

Опубликована: Май 9, 2025

Drought deficit inhibits oat growth and yield. Fulvic acid (FA) can enhance plant stress tolerance, but its effects on regulating the ascorbate–glutathione cycle, chlorophyll synthesis, carbon–assimilation ability remain unclear. Therefore, this study aimed to elucidate physiological mechanisms of FA regulation drought tolerance in oats relationship with yield using drought-resistant variety Yanke 2 drought-sensitive Bayou 9. The yield, antioxidant system, capacity under were investigated by systematically assessing changes morphogenesis, intermediates, enzyme activities, carbohydrate metabolism. results showed that stress, treatment significantly promoted (leaf area, dry matter) elevated glutathione peroxidase, ascorbate reductase, dehydroascorbate reductase reduced ascorbic acid, content. In addition, increased chlorophyll, as well magnesium protoporphyrin IX, protochlorophyllin ester content, enhanced 1,5-bisphosphate ribulose carboxylase, carboxylase enzyme, 1,7-bisphosphate sestamibiose heptulose esterase, 1,6-bisphosphate fructose aldolase, sucrose synthase, phosphate invertase, neutral invertase sucrose, glucose, Overall, fulvic alleviates drought-induced damage enhancing promoting biosynthesis, improving carbon assimilation (Yanke 2) was more effective application compared (Bayou 9). This research provides valuable insight into potential a biostimulant abiotic stress.

Язык: Английский

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Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights

Remote Sensing, Год журнала: 2025, Номер 17(10), С. 1735 - 1735

Опубликована: Май 15, 2025

The potato is the third most important crop in world, and more than 375 million metric tonnes of potatoes are produced globally on an annual basis. Potato Virus Y (PVY) poses a significant threat to production seed potatoes, resulting economic losses risks food security. Current detection methods for PVY typically rely serological assays leaves PCR tubers; however, these processes labor-intensive, time-consuming, not scalable. In this proof-of-concept study, we propose use unmanned aerial vehicles (UAVs) integrated with hyperspectral cameras, including downwelling irradiance sensor, detect commercial growers’ fields. We used 400–1000 nm visible near-infrared (Vis-NIR) camera trained several standard machine learning deep models optimized hyperparameters curated dataset. performance promising, convolutional neural network (CNN) achieving recall 0.831, reliably identifying PVY-infected plants. Notably, UAV-based imaging maintained levels comparable ground-based methods, supporting its practical viability. captures wide range spectral bands, many which redundant PVY. Our analysis identified five key regions that informative Two them spectrum, two one red-edge spectrum. This research shows early-season feasible using UAV imaging, offering potential minimize yield losses. It also highlights relevant carry distinctive signatures demonstrates feasibility provides guidance developing cost-effective multispectral sensors tailored task.

Язык: Английский

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Metabolome selection for enhancing abiotic stress resilience: advances in phenomics, prospects and challenges for breeding applications

Plant Physiology Reports, Год журнала: 2025, Номер unknown

Опубликована: Май 26, 2025

Язык: Английский

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Utilizing UAV-based high-throughput phenotyping and machine learning to evaluate drought resistance in wheat germplasm

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 237, С. 110602 - 110602

Опубликована: Май 28, 2025

Язык: Английский

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Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials

Agronomy, Год журнала: 2025, Номер 15(6), С. 1315 - 1315

Опубликована: Май 28, 2025

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes later-stage big (BP) trials. Genomic (G) were combined with hyperspectral (H) multispectral + thermal (M) imaging across the 2022 2023 growing seasons at Plant Science Research Education Unit, Citra, Florida. A panel of 312 genotypes was analyzed using GBLUP-based models, integrating G H M SP BP yield. models demonstrated promising predictive ability, achieving moderate within-year (0.43 0.51) across-year (0.43) accuracies, while reached 0.53 0.58 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy 0.47 when SP, G, used 2023. Additionally, top 25% specificity (coincide index) evaluated, showing up 47–51% within a year 43–45% between years overlap highest predicted-yielding lines trials, further emphasizing potential for early selection. These findings suggest that provide meaningful predictions yields, enabling earlier faster

Язык: Английский

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Bridging real and simulated data for cross-spatial- resolution vegetation segmentation with application to rice crops

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 218, С. 133 - 150

Опубликована: Окт. 28, 2024

Язык: Английский

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