Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics DOI Creative Commons
Weiliang Wen, Shenghao Gu, Ying Zhang

et al.

Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional genomics, digital breeding, and smart cultivation. Despite this advancement, lack of standards creation usage technology equipment become a bottleneck, limiting industry's high-quality development. This paper begins with an overview phenotyping industry presents industrial mapping big data phenomics. It analyzes necessity current state constructing standard framework phenotyping. Furthermore, proposes intended organizational structure goals framework. details essentials research development hardware equipment, acquisition, storage management data. Finally, it discusses promoting construction evaluation framework, aiming provide ideas developing

Language: Английский

Genomic selection in plant breeding: Key factors shaping two decades of progress DOI Creative Commons

Admas Alemu,

Johanna Åstrand, Osval A. Montesinos‐López

et al.

Molecular Plant, Journal Year: 2024, Volume and Issue: 17(4), P. 552 - 578

Published: March 12, 2024

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in past two decades, effectively accelerating genetic gains plant breeding.This article provides a holistic overview key factors that have influenced GP breeding during this period.We delved into pivotal roles training population size and diversity, their relationship with population, determining accuracy.Special emphasis was placed on optimizing size.We explored its benefits associated diminishing returns beyond an optimum size.This done while considering balance between resource allocation maximizing accuracy through current optimization algorithms.The density distribution single-nucleotide polymorphisms, level linkage disequilibrium, complexity, trait heritability, statistical machine-learning methods, non-additive effects are other vital factors.Using wheat, maize, potato as examples, we summarize effect these for various traits.The search high GP-theoretically reaching one when using Pearson's correlation metric-is active research area yet far from optimal traits.We hypothesize ultra-high sizes genotypic phenotypic datasets, effective methods support omics approaches (transcriptomics, metabolomics proteomics) coupled deep-learning algorithms could overcome boundaries limitations achieve highest possible accuracy, making selection tool breeding.

Language: Английский

Citations

79
Maize height estimation using combined unmanned aerial vehicle oblique photography and LIDAR canopy dynamic characteristics DOI
Tao Liu, Shaolong Zhu, Tianle Yang

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108685 - 108685

Published: Feb. 9, 2024

Language: Английский

Citations

21
Using high-throughput phenotyping platform MVS-Pheno to decipher the genetic architecture of plant spatial geometric 3D phenotypes for maize DOI
Sheng Wu, Ying Zhang, Yanxin Zhao

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 225, P. 109259 - 109259

Published: Aug. 1, 2024

Language: Английский

Citations

9
Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress DOI Creative Commons

Taoyu Dai,

Songtao Ban, Liyuan Han

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: March 22, 2024

Low temperature is a type of abiotic stress affecting the tomato ( Solanum lycopersicum ) growth. Understanding mechanisms and utilization exogenous substances underlying plant tolerance to cold would lay foundation for improving resilience in this important crop. Our study aiming investigate effect glycine betaine (GB) on seedlings increase low temperatures. By treating with GB under stress, we found that 30 mmol/L can significantly improve seedlings. Exogenous influence enzyme activity antioxidant defense system ROS levels leaves. The treatment presented higher Fv/Fm value photochemical compared control. Moreover, analysis high-throughput phenotyping also supported protect photosynthetic stress. In addition, proved increased content endogenous abscisic acid (ABA) decreased gibberellin (GA) levels, which protected tomatoes from Meanwhile, transcriptional showed regulated expression genes involved capacity, calcium signaling, photosynthesis activity, energy metabolism-related pathway-related plants. conclusion, our findings indicated GB, as cryoprotectant, enhance by system, hormone response pathway so on.

Language: Английский

Citations

8
Accurate and semantic 3D reconstruction of maize leaves DOI
Weiliang Wen, Sheng Wu,

Xianju Lu

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108566 - 108566

Published: Jan. 3, 2024

Language: Английский

Citations

7
Functions of Phytochrome Interacting Factors (PIFs) in Adapting Plants to Biotic and Abiotic Stresses DOI Open Access
Zhaoyang Li,

Ning Ma,

Fujun Zhang

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(4), P. 2198 - 2198

Published: Feb. 12, 2024

Plants possess the remarkable ability to sense detrimental environmental stimuli and launch sophisticated signal cascades that culminate in tailored responses facilitate their survival, transcription factors (TFs) are closely involved these processes. Phytochrome interacting (PIFs) among TFs belong basic helix–loop–helix family. PIFs initially identified have now been well established as core regulators of phytochrome-associated pathways response light plants. However, a growing body evidence has unraveled also play crucial role adapting plants various biological pressures. In this review, we summarize highlight function hub integrates multiple cues, including abiotic (i.e., drought, temperature, salinity) biotic stresses optimize plant growth development. not only reprogram expression related genes, but interact with adapt harsh environments. This review will contribute understanding multifaceted functions different stress conditions, which shed on efforts further dissect novel PIFs, especially adaption environments for better survival

Language: Английский

Citations

6
Combining high-throughput deep learning phenotyping and GWAS to reveal genetic variants of fruit branch angle in upland cotton DOI
Libei Li, Hui Chang,

Shuqi Zhao

et al.

Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 220, P. 119180 - 119180

Published: July 13, 2024

Language: Английский

Citations

6
Precision Phenotyping in Crop Science: From Plant Traits to Gene Discovery for Climate‐Smart Agriculture DOI Open Access

R. L. Visakh,

Sreekumar Anand,

S. Bhaskar Reddy

et al.

Plant Breeding, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 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.

Language: Английский

Citations

6
Plant microphenotype: from innovative imaging to computational analysis DOI Creative Commons
Ying Zhang, Shenghao Gu, Jianjun Du

et al.

Plant Biotechnology Journal, Journal Year: 2024, Volume and Issue: 22(4), P. 802 - 818

Published: Jan. 13, 2024

Summary The microphenotype plays a key role in bridging the gap between genotype and complex macro phenotype. In this article, we review advances data acquisition intelligent analysis of plant microphenotyping present applications science over past two decades. We then point out several challenges field suggest that cross‐scale image strategies, powerful artificial intelligence algorithms, advanced genetic analysis, computational phenotyping need to be established performed better understand interactions among genotype, environment, management. Microphenotyping has entered era 3.0 will largely advance functional genomics science.

Language: Английский

Citations

5
Analysis of Phenotypic and Physiological Characteristics of Plant Height Difference in Alfalfa DOI Creative Commons
Jing Fang,

Shangli Shi,

Yun A

et al.

Agronomy, Journal Year: 2023, Volume and Issue: 13(7), P. 1744 - 1744

Published: June 28, 2023

Cultivating new alfalfa (Medicago sativa L.) varieties with high yield and quality is of great significance for improving promoting the development grass livestock industry. Plant height an important indicator closely related to photosynthetic capacity, harvest index yield. However, underlying cause variation in among plants not clear. In this paper, we measured phenotypic traits, physiology endogenous hormone content tall- short-stalked materials analyzed external internal factors that caused difference plant alfalfa. We found traits showed significant differences, dwarf shortening main stem internode length. There were also some differences light physiological indicators contents between materials. Through correlation analysis, significantly correlated number internodes, diameter, average length, leaf–stem ratio, leaf area, Pn (net rate), Tr (transpiration upper SP (soluble protein), Suc (sucrose) content, middle Sta (starch) ZT (zeatin) IAA (indole-3-acetic acid). Further analysis Tr, LA played a direct role height, contributing most followed by IAA. Finally, starch had impact on through principal component analysis. These results provide insights into formation genetic improvement leguminous forages such as

Language: Английский

Citations

13