1
|
Renó V, Cardellicchio A, Romanjenko BC, Guadagno CR. AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L. FRONTIERS IN PLANT SCIENCE 2024; 14:1305292. [PMID: 38449576 PMCID: PMC10915054 DOI: 10.3389/fpls.2023.1305292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/21/2023] [Indexed: 03/08/2024]
Abstract
Introduction Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. Methods In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Results Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Discussion Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.
Collapse
Affiliation(s)
- Vito Renó
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy (CNR STIIMA), Bari, Italy
| | - Angelo Cardellicchio
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy (CNR STIIMA), Bari, Italy
| | | | - Carmela Rosaria Guadagno
- Department of Botany, University of Wyoming, Laramie, WY, United States
- Science Initiative, University of Wyoming, Laramie, WY, United States
| |
Collapse
|
2
|
Shoaib M, Shah B, Sayed N, Ali F, Ullah R, Hussain I. Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection. FRONTIERS IN PLANT SCIENCE 2023; 14:1283235. [PMID: 37900739 PMCID: PMC10612337 DOI: 10.3389/fpls.2023.1283235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023]
Abstract
Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.
Collapse
Affiliation(s)
- Muhammad Shoaib
- Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
| | - Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Nasir Sayed
- Department of Computer Science, Islamia College Peshawar, Peshawar, Pakistan
| | - Farman Ali
- Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Rafi Ullah
- Department of Medical Laboratory Technology, Riphah International University, Islamabad, Pakistan
| | - Irfan Hussain
- Centre for Autonomous Robotic Systems, Khalifa University, Abu Dhabi, United Arab Emirates
| |
Collapse
|
3
|
Paliwal S, Tripathi MK, Tiwari S, Tripathi N, Payasi DK, Tiwari PN, Singh K, Yadav RK, Asati R, Chauhan S. Molecular Advances to Combat Different Biotic and Abiotic Stresses in Linseed ( Linum usitatissimum L.): A Comprehensive Review. Genes (Basel) 2023; 14:1461. [PMID: 37510365 PMCID: PMC10379177 DOI: 10.3390/genes14071461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/11/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Flax, or linseed, is considered a "superfood", which means that it is a food with diverse health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or indirect impact on plant health are experienced by flax plants as a result of changing environmental circumstances. Research on the impact of various stresses and their possible ameliorators is prompted by such expectations. By inducing the loss of specific alleles and using a limited number of selected varieties, modern breeding techniques have decreased the overall genetic variability required for climate-smart agriculture. However, gene banks have well-managed collectionns of landraces, wild linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits. Applications of molecular markers in modern breeding have made it easy to identify quantitative trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance, and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, and alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement, are the primary topics of this review.
Collapse
Affiliation(s)
- Shruti Paliwal
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Manoj Kumar Tripathi
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Sushma Tiwari
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Niraj Tripathi
- Directorate of Research Services, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004, India
| | - Devendra K Payasi
- All India Coordinated Research Project on Linseed, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Regional Agricultural Research Station, Sagar 470001, India
| | - Prakash N Tiwari
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Kirti Singh
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Rakesh Kumar Yadav
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Ruchi Asati
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Shailja Chauhan
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| |
Collapse
|
4
|
Genangeli A, Avola G, Bindi M, Cantini C, Cellini F, Grillo S, Petrozza A, Riggi E, Ruggiero A, Summerer S, Tedeschi A, Gioli B. Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1730. [PMID: 37111953 PMCID: PMC10143644 DOI: 10.3390/plants12081730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/19/2023]
Abstract
Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.
Collapse
Affiliation(s)
- Andrea Genangeli
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Giovanni Avola
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Marco Bindi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Claudio Cantini
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Francesco Cellini
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Stefania Grillo
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Angelo Petrozza
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Ezio Riggi
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Alessandra Ruggiero
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Stephan Summerer
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Anna Tedeschi
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Beniamino Gioli
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| |
Collapse
|
5
|
Choudhary P, Pramitha L, Aggarwal PR, Rana S, Vetriventhan M, Muthamilarasan M. Biotechnological interventions for improving the seed longevity in cereal crops: progress and prospects. Crit Rev Biotechnol 2023; 43:309-325. [PMID: 35443842 DOI: 10.1080/07388551.2022.2027863] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Seed longevity is a measure of the viability of seeds during long-term storage and is crucial for germplasm conservation and crop improvement programs. Also, longevity is an important trait for ensuring food and nutritional security. Thus, a better understanding of various factors regulating seed longevity is requisite to improve this trait and to minimize the genetic drift during the regeneration of germplasm. In particular, seed deterioration of cereal crops during storage adversely affects agricultural productivity and food security. The irreversible process of seed deterioration involves a complex interplay between different genes and regulatory pathways leading to: loss of DNA integrity, membrane damage, inactivation of storage enzymes and mitochondrial dysfunction. Identifying the genetic determinants of seed longevity and manipulating them using biotechnological tools hold the key to ensuring prolonged seed storage. Genetics and genomics approaches had identified several genomic regions regulating the longevity trait in major cereals such as: rice, wheat, maize and barley. However, very few studies are available in other Poaceae members, including millets. Deploying omics tools, including genomics, proteomics, metabolomics, and phenomics, and integrating the datasets will pinpoint the precise molecular determinants affecting the survivability of seeds. Given this, the present review enumerates the genetic factors regulating longevity and demonstrates the importance of integrated omics strategies to dissect the molecular machinery underlying seed deterioration. Further, the review provides a roadmap for deploying biotechnological approaches to manipulate the genes and genomic regions to develop improved cultivars with prolonged storage potential.
Collapse
Affiliation(s)
- Pooja Choudhary
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Lydia Pramitha
- School of Agriculture and Biosciences, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Pooja Rani Aggarwal
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Sumi Rana
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Mani Vetriventhan
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India
| | | |
Collapse
|
6
|
Colpo A, Demaria S, Baldisserotto C, Pancaldi S, Brestič M, Živčak M, Ferroni L. Long-Term Alleviation of the Functional Phenotype in Chlorophyll-Deficient Wheat and Impact on Productivity: A Semi-Field Phenotyping Experiment. PLANTS (BASEL, SWITZERLAND) 2023; 12:822. [PMID: 36840171 PMCID: PMC9964019 DOI: 10.3390/plants12040822] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
Wheat mutants with a reduced chlorophyll synthesis are affected by a defective control of the photosynthetic electron flow, but tend to recover a wild-type phenotype. The sensitivity of some mutants to light fluctuations suggested that cultivation outdoors could significantly impact productivity. Six mutant lines of Triticum durum or Triticum aestivum with their respective wild-type cultivars were cultivated with a regular seasonal cycle (October-May) in a semi-field experiment. Leaf chlorophyll content and fluorescence parameters were analysed at the early (November) and late (May) developmental stages, and checked for correlation with morphometric and grain-production parameters. The alleviation of the phenotype severity concerned primarily the recovery of the photosynthetic-membrane functionality, but not the leaf chlorophyll content. Photosystem II (PSII) was less photoprotected in the mutants, but a moderate PSII photoinhibition could help control the electron flow into the chain. The accumulation of interchain electron carriers was a primary acclimative response towards the naturally fluctuating environment, maximally exploited by the mature durum-wheat mutants. The mutation itself and/or the energy-consuming compensatory mechanisms markedly influenced the plant morphogenesis, leading especially to reduced tillering, which in turn resulted in lower grain production per plant. Consistently with the interrelation between early photosynthetic phenotype and grain-yield per plant, chlorophyll-fluorescence indexes related to the level of photoprotective thermal dissipation (pNPQ), photosystem II antenna size (ABS/RC), and pool of electron carriers (Sm) are proposed as good candidates for the in-field phenotyping of chlorophyll-deficient wheat.
Collapse
Affiliation(s)
- Andrea Colpo
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Sara Demaria
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Costanza Baldisserotto
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Simonetta Pancaldi
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Marian Brestič
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Marek Živčak
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Lorenzo Ferroni
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| |
Collapse
|
7
|
Devi J, Sagar V, Mishra GP, Jha PK, Gupta N, Dubey RK, Singh PM, Behera TK, Prasad PVV. Heat stress tolerance in peas ( Pisum sativum L.): Current status and way forward. FRONTIERS IN PLANT SCIENCE 2023; 13:1108276. [PMID: 36733601 PMCID: PMC9887200 DOI: 10.3389/fpls.2022.1108276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
In the era of climate change, the overall productivity of pea (Pisum sativum L.) is being threatened by several abiotic stresses including heat stress (HS). HS causes severe yield losses by adversely affecting several traits in peas. A reduction in pod yield has been reported from 11.1% to 17.5% when mean daily temperature increase from 1.4 to 2.2°C. High-temperature stress (30.5-33°C) especially during reproductive phase is known to drastically reduce both seed yield and germination. HS during germination and early vegetative stage resulted in poor emergence and stunted plant growth along with detrimental effects on physiological functions of the pea plant. To combat HS and continue its life cycle, plants use various defense strategies including heat escape, avoidance or tolerance mechanisms. Ironically, the threshold temperatures for pea plant and its responses are inconsistent and not yet clearly identified. Trait discovery through traditional breeding such as semi leaflessness (afila), upright growing habit, lodging tolerance, lower canopy temperature and small seeded nature has highlighted their utility for greater adaptation under HS in pea. Screening of crop gene pool and landraces for HS tolerance in a targeted environment is a simple approach to identify HS tolerant genotypes. Thus, precise phenotyping using modern phenomics tools could lead to increased breeding efficiency. The NGS (next generation sequencing) data can be associated to find the candidate genes responsible for the HS tolerance in pea. In addition, genomic selection, genome wide association studies (GWAS) and marker assisted selection (MAS) can be used for the development of HS tolerant pea genotypes. Additionally, development of transgenics could be an alternative strategy for the development of HS tolerant pea genotypes. This review comprehensively covers the various aspects of HS tolerance mechanisms in the pea plant, screening protocols, omic advances, and future challenges for the development of HS tolerant genotypes.
Collapse
Affiliation(s)
- Jyoti Devi
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - Vidya Sagar
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - Gyan P. Mishra
- Indian Council of Agricultural Research-Indian Agricultural Research Institute, Pusa, New Delhi, India
| | - Prakash Kumar Jha
- Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification, Kansas State University, Manhattan, KS, United States
| | - Nakul Gupta
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - Rakesh K. Dubey
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - Prabhakar M. Singh
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - Tusar K. Behera
- Indian Council of Agricultural Research-Indian Institute of Vegetable Research, Jakhini, Varanasi, India
| | - P. V. Vara Prasad
- Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification, Kansas State University, Manhattan, KS, United States
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| |
Collapse
|
8
|
Marzougui A, McGee RJ, Van Vleet S, Sankaran S. Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics. FRONTIERS IN PLANT SCIENCE 2023; 14:1111575. [PMID: 37152173 PMCID: PMC10161932 DOI: 10.3389/fpls.2023.1111575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/06/2023] [Indexed: 05/09/2023]
Abstract
Introduction Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model. Methods The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points. Results and discussion The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.
Collapse
Affiliation(s)
- Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Rebecca J. McGee
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, United States
| | - Stephen Van Vleet
- Agriculture and Natural Resources, Washington State University Extension, Colfax, WA, United States
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
- *Correspondence: Sindhuja Sankaran,
| |
Collapse
|
9
|
Ali A, Altaf MT, Nadeem MA, Karaköy T, Shah AN, Azeem H, Baloch FS, Baran N, Hussain T, Duangpan S, Aasim M, Boo KH, Abdelsalam NR, Hasan ME, Chung YS. Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes. FRONTIERS IN PLANT SCIENCE 2022; 13:952759. [PMID: 36247536 PMCID: PMC9554552 DOI: 10.3389/fpls.2022.952759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/12/2022] [Indexed: 06/16/2023]
Abstract
The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.
Collapse
Affiliation(s)
- Amjad Ali
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Muhammad Tanveer Altaf
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Tolga Karaköy
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Adnan Noor Shah
- Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Hajra Azeem
- Department of Plant Pathology, Faculty of Agricultural Sciences & Technology, Bahauddin Zakariya University, Multan, Pakistan
| | - Faheem Shehzad Baloch
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Nurettin Baran
- Bitkisel Uretim ve Teknolojileri Bolumu, Uygulamali Bilimler Faku Itesi, Mus Alparslan Universitesi, Mus, Turkey
| | - Tajamul Hussain
- Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Thailand
| | - Saowapa Duangpan
- Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Thailand
| | - Muhammad Aasim
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Kyung-Hwan Boo
- Subtropical/Tropical Organism Gene Bank, Department of Biotechnology, College of Applied Life Science, Jeju National University, Jeju, South Korea
| | - Nader R. Abdelsalam
- Agricultural Botany Department, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria, Egypt
| | - Mohamed E. Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju, South Korea
| |
Collapse
|
10
|
Sandhu KS, Shiv A, Kaur G, Meena MR, Raja AK, Vengavasi K, Mall AK, Kumar S, Singh PK, Singh J, Hemaprabha G, Pathak AD, Krishnappa G, Kumar S. Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane. PLANTS 2022; 11:plants11162139. [PMID: 36015442 PMCID: PMC9412483 DOI: 10.3390/plants11162139] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/08/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022]
Abstract
Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
Collapse
Affiliation(s)
- Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163, USA
| | - Aalok Shiv
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Mintu Ram Meena
- Regional Center, ICAR-Sugarcane Breeding Institute, Karnal 132001, India
| | - Arun Kumar Raja
- Division of Crop Production, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Krishnapriya Vengavasi
- Division of Crop Production, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Ashutosh Kumar Mall
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Sanjeev Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Praveen Kumar Singh
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Jyotsnendra Singh
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Ashwini Dutt Pathak
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Gopalareddy Krishnappa
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
- Correspondence: (G.K.); (S.K.)
| | - Sanjeev Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
- Correspondence: (G.K.); (S.K.)
| |
Collapse
|
11
|
Guo Y, Quan J, Wang X, Zhang Z, Liu X, Zhang R, Yue M. Predictability of parental ultraviolet-B environment shapes the growth strategies of clonal Glechoma longituba. FRONTIERS IN PLANT SCIENCE 2022; 13:949752. [PMID: 35991455 PMCID: PMC9386186 DOI: 10.3389/fpls.2022.949752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
Although there is an increasing debate about ecological consequences of environmental predictability for plant phenotype and fitness, the effect of predictability of parental environments on the offspring is still indefinite. To clarify the role of environmental predictability in maternal effects and the growth strategy of clonal offspring, a greenhouse experiment was conducted with Glechoma longituba. The parental ramets were arranged in three ultraviolet-B (UV-B) conditions, representing two predictable environments (regular and enhanced UV-B) and an unpredictable environment (random UV-B), respectively. The offspring environments were the same as their parent or not (without UV-B). At the end of experiment, the growth parameters of offspring were analyzed. The results showed that maternal effects and offspring growth were regulated by environmental predictability. Offspring of unpredictable environmental parents invested more resources in improving defense components rather than in rapid growth. Although offspring of predictable parents combined two processes of defense and growth, there were still some differences in the strategies between the two offspring, and the offspring of regular parent increased the biomass allocation to roots (0.069 g of control vs. 0.092 g of regular), but that of enhanced parent changed the resource allocation of nitrogen in roots and phosphorus in blade. Moreover, when UV-B environments of parent and offspring were matched, it seemed that maternal effects were not adaptive, while the growth inhibition in the predictable environment was weaker than that in unpredictable environment. In the predictable environment, the recovered R/S and the increased defense substances (flavonoid and anthocyanin) contributed to improving offspring fitness. In addition, when UV-B environments of parent and offspring were mismatched, offspring growth was restored or improved to some extent. The offspring performance in mismatched environments was controlled by both transgenerational effect and within-generational plasticity. In summary, the maternal effects affected growth strategies of offspring, and the differences of strategies depended on the predictability of parental UV-B environments, the clone improved chemical defense to cope with unpredictable environments, while the growth and defense could be balanced in predictable environments. The anticipatory maternal effects were likely to improve the UV-B resistance.
Collapse
|
12
|
Yadav B, Kaur V, Narayan OP, Yadav SK, Kumar A, Wankhede DP. Integrated omics approaches for flax improvement under abiotic and biotic stress: Current status and future prospects. FRONTIERS IN PLANT SCIENCE 2022; 13:931275. [PMID: 35958216 PMCID: PMC9358615 DOI: 10.3389/fpls.2022.931275] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 05/03/2023]
Abstract
Flax (Linum usitatissimum L.) or linseed is one of the important industrial crops grown all over the world for seed oil and fiber. Besides oil and fiber, flax offers a wide range of nutritional and therapeutic applications as a feed and food source owing to high amount of α-linolenic acid (omega-3 fatty acid), lignans, protein, minerals, and vitamins. Periodic losses caused by unpredictable environmental stresses such as drought, heat, salinity-alkalinity, and diseases pose a threat to meet the rising market demand. Furthermore, these abiotic and biotic stressors have a negative impact on biological diversity and quality of oil/fiber. Therefore, understanding the interaction of genetic and environmental factors in stress tolerance mechanism and identification of underlying genes for economically important traits is critical for flax improvement and sustainability. In recent technological era, numerous omics techniques such as genomics, transcriptomics, metabolomics, proteomics, phenomics, and ionomics have evolved. The advancements in sequencing technologies accelerated development of genomic resources which facilitated finer genetic mapping, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection in major cereal and oilseed crops including flax. Extensive studies in the area of genomics and transcriptomics have been conducted post flax genome sequencing. Interestingly, research has been focused more for abiotic stresses tolerance compared to disease resistance in flax through transcriptomics, while the other areas of omics such as metabolomics, proteomics, ionomics, and phenomics are in the initial stages in flax and several key questions remain unanswered. Little has been explored in the integration of omic-scale data to explain complex genetic, physiological and biochemical basis of stress tolerance in flax. In this review, the current status of various omics approaches for elucidation of molecular pathways underlying abiotic and biotic stress tolerance in flax have been presented and the importance of integrated omics technologies in future research and breeding have been emphasized to ensure sustainable yield in challenging environments.
Collapse
Affiliation(s)
- Bindu Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Vikender Kaur
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Om Prakash Narayan
- College of Arts and Sciences, University of Florida, Gainesville, FL, United States
| | - Shashank Kumar Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Ashok Kumar
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | | |
Collapse
|
13
|
Safder I, Shao G, Sheng Z, Hu P, Tang S. Genome-wide identification studies - A primer to explore new genes in plant species. PLANT BIOLOGY (STUTTGART, GERMANY) 2022; 24:9-22. [PMID: 34558163 DOI: 10.1111/plb.13340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.
Collapse
Affiliation(s)
- I Safder
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - G Shao
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - Z Sheng
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - P Hu
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - S Tang
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| |
Collapse
|
14
|
Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK. Systems biology for crop improvement. THE PLANT GENOME 2021; 14:e20098. [PMID: 33949787 DOI: 10.1002/tpg2.20098] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/09/2021] [Indexed: 05/19/2023]
Abstract
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.
Collapse
Affiliation(s)
- Lekha T Pazhamala
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - A Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology and School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
- State Agricultural Biotechnology Centre, Crop Research Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
| |
Collapse
|
15
|
Raza A, Tabassum J, Kudapa H, Varshney RK. Can omics deliver temperature resilient ready-to-grow crops? Crit Rev Biotechnol 2021; 41:1209-1232. [PMID: 33827346 DOI: 10.1080/07388551.2021.1898332] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Plants are extensively well-thought-out as the main source for nourishing natural life on earth. In the natural environment, plants have to face several stresses, mainly heat stress (HS), chilling stress (CS) and freezing stress (FS) due to adverse climate fluctuations. These stresses are considered as a major threat for sustainable agriculture by hindering plant growth and development, causing damage, ultimately leading to yield losses worldwide and counteracting to achieve the goal of "zero hunger" proposed by the Food and Agricultural Organization (FAO) of the United Nations. Notably, this is primarily because of the numerous inequities happening at the cellular, molecular and/or physiological levels, especially during plant developmental stages under temperature stress. Plants counter to temperature stress via a complex phenomenon including variations at different developmental stages that comprise modifications in physiological and biochemical processes, gene expression and differences in the levels of metabolites and proteins. During the last decade, omics approaches have revolutionized how plant biologists explore stress-responsive mechanisms and pathways, driven by current scientific developments. However, investigations are still required to explore numerous features of temperature stress responses in plants to create a complete idea in the arena of stress signaling. Therefore, this review highlights the recent advances in the utilization of omics approaches to understand stress adaptation and tolerance mechanisms. Additionally, how to overcome persisting knowledge gaps. Shortly, the combination of integrated omics, genome editing, and speed breeding can revolutionize modern agricultural production to feed millions worldwide in order to accomplish the goal of "zero hunger."
Collapse
Affiliation(s)
- Ali Raza
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan, China
| | - Javaria Tabassum
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Science (CAAS), Hangzhou, China
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.,The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia
| |
Collapse
|
16
|
Raza A, Razzaq A, Mehmood SS, Hussain MA, Wei S, He H, Zaman QU, Xuekun Z, Hasanuzzaman M. Omics: The way forward to enhance abiotic stress tolerance in Brassica napus L. GM CROPS & FOOD 2021; 12:251-281. [PMID: 33464960 PMCID: PMC7833762 DOI: 10.1080/21645698.2020.1859898] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Plant abiotic stresses negative affects growth and development, causing a massive reduction in global agricultural production. Rapeseed (Brassica napus L.) is a major oilseed crop because of its economic value and oilseed production. However, its productivity has been reduced by many environmental adversities. Therefore, it is a prime need to grow rapeseed cultivars, which can withstand numerous abiotic stresses. To understand the various molecular and cellular mechanisms underlying the abiotic stress tolerance and improvement in rapeseed, omics approaches have been extensively employed in recent years. This review summarized the recent advancement in genomics, transcriptomics, proteomics, metabolomics, and their imploration in abiotic stress regulation in rapeseed. Some persisting bottlenecks have been highlighted, demanding proper attention to fully explore the omics tools. Further, the potential prospects of the CRISPR/Cas9 system for genome editing to assist molecular breeding in developing abiotic stress-tolerant rapeseed genotypes have also been explained. In short, the combination of integrated omics, genome editing, and speed breeding can alter rapeseed production worldwide.
Collapse
Affiliation(s)
- Ali Raza
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture , Faisalabad, Pakistan
| | - Sundas Saher Mehmood
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Muhammad Azhar Hussain
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Su Wei
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Huang He
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Qamar U Zaman
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
| | - Zhang Xuekun
- College of Agriculture, Engineering Research Center of Ecology and Agricultural Use of Wetland of Ministry of Education, Yangtze University Jingzhou , China
| | - Mirza Hasanuzzaman
- Department of Agronomy, Faculty of Agriculture, Sher-e-Bangla Agricultural University , Dhaka, Bangladesh
| |
Collapse
|
17
|
Guimarães PHR, de Lima IP, de Castro AP, Lanna AC, Guimarães Santos Melo P, de Raïssac M. Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought? RICE (NEW YORK, N.Y.) 2020; 13:67. [PMID: 32930888 PMCID: PMC7492358 DOI: 10.1186/s12284-020-00404-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 06/23/2020] [Indexed: 05/13/2023]
Abstract
BACKGROUND The root system plays a major role in plant growth and development and root system architecture is reported to be the main trait related to plant adaptation to drought. However, phenotyping root systems in situ is not suited to high-throughput methods, leading to the development of non-destructive methods for evaluations in more or less controlled root environments. This study used a root phenotyping platform with a panel of 20 japonica rice accessions in order to: (i) assess their genetic diversity for a set of structural and morphological root traits and classify the different types; (ii) analyze the plastic response of their root system to a water deficit at reproductive phase and (iii) explore the ability of the platform for high-throughput phenotyping of root structure and morphology. RESULTS High variability for the studied root traits was found in the reduced set of accessions. Using eight selected traits under irrigated conditions, five root clusters were found that differed in root thickness, branching index and the pattern of fine and thick root distribution along the profile. When water deficit occurred at reproductive phase, some accessions significantly reduced root growth compared to the irrigated treatment, while others stimulated it. It was found that root cluster, as defined under irrigated conditions, could not predict the plastic response of roots under drought. CONCLUSIONS This study revealed the possibility of reconstructing the structure of root systems from scanned images. It was thus possible to significantly class root systems according to simple structural traits, opening up the way for using such a platform for medium to high-throughput phenotyping. The study also highlighted the uncoupling between root structures under non-limiting water conditions and their response to drought.
Collapse
Affiliation(s)
| | - Isabela Pereira de Lima
- Universidade Federal de Lavras, Departamento de Agricultura, Campus Universitário, Lavras, MG, 37200-000, Brazil
| | | | - Anna Cristina Lanna
- Embrapa Arroz e Feijão, Rodovia GO-462, km 12, Santo Antônio de Goiás, GO, 75375-000, Brazil
| | | | - Marcel de Raïssac
- Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, AGAP, Montpellier, France.
| |
Collapse
|
18
|
An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping. REMOTE SENSING 2020. [DOI: 10.3390/rs12101540] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.
Collapse
|
19
|
Nair RM, Pandey AK, War AR, Hanumantharao B, Shwe T, Alam AKMM, Pratap A, Malik SR, Karimi R, Mbeyagala EK, Douglas CA, Rane J, Schafleitner R. Biotic and Abiotic Constraints in Mungbean Production-Progress in Genetic Improvement. FRONTIERS IN PLANT SCIENCE 2019; 10:1340. [PMID: 31736995 PMCID: PMC6829579 DOI: 10.3389/fpls.2019.01340] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 09/25/2019] [Indexed: 05/22/2023]
Abstract
Mungbean [Vigna radiata (L.) R. Wilczek var. radiata] is an important food and cash legume crop in Asia. Development of short duration varieties has paved the way for the expansion of mungbean into other regions such as Sub-Saharan Africa and South America. Mungbean productivity is constrained by biotic and abiotic factors. Bruchids, whitefly, thrips, stem fly, aphids, and pod borers are the major insect-pests. The major diseases of mungbean are yellow mosaic, anthracnose, powdery mildew, Cercospora leaf spot, halo blight, bacterial leaf spot, and tan spot. Key abiotic stresses affecting mungbean production are drought, waterlogging, salinity, and heat stress. Mungbean breeding has been critical in developing varieties with resistance to biotic and abiotic factors, but there are many constraints still to address that include the precise and accurate identification of resistance source(s) for some of the traits and the traits conferred by multi genes. Latest technologies in phenotyping, genomics, proteomics, and metabolomics could be of great help to understand insect/pathogen-plant, plant-environment interactions and the key components responsible for resistance to biotic and abiotic stresses. This review discusses current biotic and abiotic constraints in mungbean production and the challenges in genetic improvement.
Collapse
Affiliation(s)
- Ramakrishnan M. Nair
- World Vegetable Center, South Asia, Hyderabad, India
- *Correspondence: Ramakrishnan M. Nair,
| | | | - Abdul R. War
- World Vegetable Center, South Asia, Hyderabad, India
| | | | - Tun Shwe
- Myanmar Department of Agricultural Research, Nay Pyi Taw, Myanmar
| | - AKMM Alam
- Pulses Research Centre, Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh
| | - Aditya Pratap
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | | | - Rael Karimi
- Kenya Agricultural and Livestock Research Organization (KALRO), Katumani, Kenya
| | - Emmanuel K. Mbeyagala
- National Agricultural Research Organization-National Semi-Arid Resources Research Institute (NARO-NaSARRI), Soroti, Uganda
| | - Colin A. Douglas
- Agri-Science Queensland, Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Jagadish Rane
- National Institute of Abiotic Stress Management, Baramati, India
| | | |
Collapse
|