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Spanò R, Petrozza A, Summerer S, Fortunato S, de Pinto MC, Cellini F, Mascia T. Overview of transcriptome changes and phenomic profile of sanitized artichoke vis-à-vis non-sanitized plants. PLANT BIOLOGY (STUTTGART, GERMANY) 2024; 26:715-726. [PMID: 38924230 DOI: 10.1111/plb.13675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/19/2024] [Indexed: 06/28/2024]
Abstract
Plant tissue in vitro culture is increasingly used in agriculture to improve crop production, nutritional quality, and commercial value. In plant virology, the technique is used as sanitation protocol to produce virus-free plants. Sanitized (S) artichokes show increased vigour compared to their non-sanitized (NS) counterparts, because viral infections lead to a decline of growth and development. To investigate mechanisms that control the complex traits related to morphology, growth, and yield in S artichokes compared to NS plants, RNAseq analysis and phenotyping by imaging were used. The role of peroxidases (POD) was also investigated to understand their involvement in sanitized plant development. Results showed that virus infection affected regulation of cell cycle, gene expression and signal transduction modulating cellular response to stimulus/stress. Moreover, primary metabolism and photosynthesis were also influenced, contributing to explain the main morphological differences observed between S and NS artichokes. Sanitized artichokes are also characterized by higher POD activity, probably associated with increased plant growth, rather than strengthening of cell walls. Overall, results show that the differences in development of S artichokes may be derived from the in vitro culture stressor, as well as through pathogen elimination, which, in turn, improve qualitative and quantitative artichoke production.
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Affiliation(s)
- R Spanò
- Department of Soil, Plant and Food Sciences, University of Bari "Aldo Moro", Bari, Italy
| | - A Petrozza
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - S Summerer
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - S Fortunato
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", Bari, Italy
| | - M C de Pinto
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", Bari, Italy
| | - F Cellini
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - T Mascia
- Department of Soil, Plant and Food Sciences, University of Bari "Aldo Moro", Bari, Italy
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Jené L, Munné-Bosch S. Hormonal involvement in boosting the vegetative vigour underlying caffeine-related improvements in lentil production. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 336:111856. [PMID: 37660891 DOI: 10.1016/j.plantsci.2023.111856] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/05/2023]
Abstract
Previous studies have shown that caffeine (1,3,7-trimethylxanthine) has some potential for its use as a biostimulant ingredient for boosting lentil production at suboptimal temperatures. However, some limitations to its use include its potential side effects as an emerging contaminant and the current lack of knowledge of its mechanism of action. Here, we aimed to study the mechanisms underlying improved lentil production upon caffeine application. Greenhouse-grown plants treated with caffeine (at 10-5 M, 10-4 M, and 10-3 M) were compared to an untreated, control treatment, and both reproductive and vegetative vigour were evaluated in parallel with endogenous foliar concentrations of phytohormones, including both stress and growth-related hormones. Results showed an enhanced lentil production at the highest caffeine concentration (10-3 M) which might be attributed, at least in part, to a greater vegetative vigour. The hormonal profiling revealed a dual effect. Firstly, there was a specific increase in jasmonoyl-isoleucine (JA-Ile) in the short term, which may provide a priming effect. Secondly, abscisic acid (ABA) content kept at low levels and the active cytokinin (CK) isopentenyl adenine (2-iP) increased and persisted at high levels throughout the reproductive stage. Cytokinin-mediated effects on growth, and more specifically the high CK/ABA ratios in leaves, appeared to mediate caffeine-related effects in boosting vegetative vigour. In conclusion, caffeine emerges as a compelling alkaloid for integration into biostimulant formulations due to its favorable effect in boosting lentil production through an improvement of vegetative vigour. These outcomes appear to be modulated by phytohormones, most notably jasmonates, priming plants for improved performance under suboptimal temperatures, and cytokinins, alongside ABA and its associated ratios, collectively enhancing plant growth and reproductive vigour in challenging conditions.
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Affiliation(s)
- Laia Jené
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Spain
| | - Sergi Munné-Bosch
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Spain; Institute of Nutrition and Food Safety (INSA), University of Barcelona, Barcelona, Spain.
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3
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Yang C, Baireddy S, Méline V, Cai E, Caldwell D, Iyer-Pascuzzi AS, Delp EJ. Image-based plant wilting estimation. PLANT METHODS 2023; 19:52. [PMID: 37254098 DOI: 10.1186/s13007-023-01026-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/07/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress. RESULTS We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress. CONCLUSION Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.
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Affiliation(s)
- Changye Yang
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN, 47907, USA.
| | - Sriram Baireddy
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN, 47907, USA
| | - Valérian Méline
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN, 47907, USA
| | - Enyu Cai
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, IN, 47907, USA
| | - Denise Caldwell
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, IN, 47907, USA
| | - Anjali S Iyer-Pascuzzi
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, IN, 47907, USA
| | - Edward J Delp
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN, 47907, USA
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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.
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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.)
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Oteng-Frimpong R, Karikari B, Sie EK, Kassim YB, Puozaa DK, Rasheed MA, Fonceka D, Okello DK, Balota M, Burow M, Ozias-Akins P. Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut ( Arachis hypogaea L.) germplasm. FRONTIERS IN PLANT SCIENCE 2023; 13:1076744. [PMID: 36684745 PMCID: PMC9849250 DOI: 10.3389/fpls.2022.1076744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.
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Affiliation(s)
- Richard Oteng-Frimpong
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
| | - Emmanuel Kofi Sie
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Yussif Baba Kassim
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Doris Kanvenaa Puozaa
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Masawudu Abdul Rasheed
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Daniel Fonceka
- Centre d’Etude Régional pour l’Amélioration de l’Adaptation àla Sécheresse (CERAAS), Institut Sénégalais de Recherches Agricoles (ISRA), Thiès, Senegal
| | - David Kallule Okello
- Oil Crops Research Program, National Semi-Arid Resources Research Institute (NaSARRI), Soroti, Uganda
| | - Maria Balota
- School of Plant and Environmental Sciences, Tidewater Agricultural Research and Extension Center (AREC), Virginia Tech, Suffolk, VA, United States
| | - Mark Burow
- Texas A&M AgriLife Research and Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, United States
| | - Peggy Ozias-Akins
- Institute of Plant Breeding Genetics and Genomics, University of Georgia, Tifton, GA, United States
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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Physiological and Structural Responses of Olive Leaves Related to Tolerance/Susceptibility to Verticillium dahliae. PLANTS 2022; 11:plants11172302. [PMID: 36079682 PMCID: PMC9459789 DOI: 10.3390/plants11172302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022]
Abstract
Verticillium wilt of olive (VWO), caused by the soil borne fungus Verticillium dahliae, is one of the most relevant diseases affecting this crop worldwide. One of the best VWO management strategies is the use of tolerant cultivars. Scarce information is available about physiological and structural responses in the leaves of olive cultivars displaying different levels of tolerance to VWO. To identify links between this phenotype and variations in functional characteristics of the leaves, this study examined the structural and physiological traits and the correlations among them in different olive varieties. This evaluation was conducted in the presence/absence of V. dahliae. On the one hand, no leaf trait but the area was related to VWO tolerance in the absence of the pathogen. On the other hand, after inoculation, susceptible cultivars showed lower leaf area and higher leaf mass per area and dry matter content. Furthermore, at the physiological level, these plants showed severe symptoms resembling water stress. Analyzing the relationships among physiological and structural traits revealed differences between tolerant and susceptible cultivars both in the absence and in the presence of V. dahliae. These results showed that olive leaves of VWO-tolerant and VWO-susceptible cultivars adopt different strategies to cope with the pathogen.
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8
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Chapu I, Okello DK, Okello RCO, Odong TL, Sarkar S, Balota M. Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding. FRONTIERS IN PLANT SCIENCE 2022; 13:912332. [PMID: 35774822 PMCID: PMC9238324 DOI: 10.3389/fpls.2022.912332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H 2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.
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Affiliation(s)
- Ivan Chapu
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | | | - Robert C. Ongom Okello
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | - Thomas Lapaka Odong
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | - Sayantan Sarkar
- Blackland Research and Extension Center, Texas A&M AgriLife Research, Temple, TX, United States
| | - Maria Balota
- School of Plant and Environmental Sciences, Tidewater AREC, Virginia Tech, Suffolk, VA, United States
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Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB Model to Micrometeorology Environment in Begonia Fimbristipula Hance. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The high quality and efficient production of greenhouse vegetation depend on micrometeorology environmental adjusting such as system warming and illumination supplement. In order to improve the quantity, quality, and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of the greenhouse environment and reduce the running costs. The parameters from the color information of the plant canopy reflect the internal physiological conditions, thus, the RGB model has been widely used in the color analysis of digital pictures of leaves. We take photographs of Begonia Fimbristipula Hance (BFH) growing in the greenhouse at a fixed time every day and measure the meteorological factors. The results showed that the color scale for the single leaf, single plant, and the populated canopy of the BFH photographs all have skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of the canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode, and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized. The models can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.
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Lejeune P, Fratamico A, Bouché F, Huerga-Fernández S, Tocquin P, Périlleux C. LED color gradient as a new screening tool for rapid phenotyping of plant responses to light quality. Gigascience 2022; 11:6515743. [PMID: 35084034 PMCID: PMC8848316 DOI: 10.1093/gigascience/giab101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/10/2021] [Accepted: 12/15/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The increasing demand for local food production is fueling high interest in the development of controlled environment agriculture. In particular, LED technology brings energy-saving advantages together with the possibility of manipulating plant phenotypes through light quality control. However, optimizing light quality is required for each cultivated plant and specific purpose. FINDINGS This article shows that the combination of LED gradient set-ups with imaging-based non-destructive plant phenotyping constitutes an interesting new screening tool with the potential to improve speed, logistics, and information output. To validate this concept, an experiment was performed to evaluate the effects of a complete range of red:blue ratios on 7 plant species: Arabidopsis thaliana, Brachypodium distachyon, Euphorbia peplus, Ocimum basilicum, Oryza sativa, Solanum lycopersicum, and Setaria viridis. Plants were exposed during 30 days to the light gradient and showed significant, but species-dependent, responses in terms of dimension, shape, and color. A time-series analysis of phenotypic descriptors highlighted growth changes but also transient responses of plant shapes to the red:blue ratio. CONCLUSION This approach, which generated a large reusable dataset, can be adapted for addressing specific needs in crop production or fundamental questions in photobiology.
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Affiliation(s)
- Pierre Lejeune
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
| | - Anthony Fratamico
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
| | - Frédéric Bouché
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
| | - Samuel Huerga-Fernández
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
| | - Pierre Tocquin
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
| | - Claire Périlleux
- InBioS - PhytoSYSTEMS, Laboratory of Plant Physiology, University of Liège, B22 Sart Tilman Campus, 4 Chemin de la Vallée, B-4000 Liège, Belgium
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Verticillium Wilt of Olive and its Control: What Did We Learn during the Last Decade? PLANTS 2020; 9:plants9060735. [PMID: 32545292 PMCID: PMC7356185 DOI: 10.3390/plants9060735] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 02/06/2023]
Abstract
Verticillium (Verticillium dahliae Kleb.) wilt is one of the most devastating diseases affecting olive (Olea europaea L. subsp. europaea var. europaea) cultivation. Its effective control strongly relies on integrated management strategies. Olive cultivation systems are experiencing important changes (e.g., high-density orchards, etc.) aiming at improving productivity. The impact of these changes on soil biology and the incidence/severity of olive pests and diseases has not yet been sufficiently evaluated. A comprehensive understanding of the biology of the pathogen and its populations, the epidemiological factors contributing to exacerbating the disease, the underlying mechanisms of tolerance/resistance, and the involvement of the olive-associated microbiota in the tree's health is needed. This knowledge will be instrumental to developing more effective control measures to confront the disease in regions where the pathogen is present, or to exclude it from V. dahliae-free areas. This review compiles the most recent advances achieved to understand the olive-V. dahliae interaction as well as measures to control the disease. Aspects such as the molecular basis of the host-pathogen interaction, the identification of new biocontrol agents, the implementation of "-omics" approaches to unravel the basis of disease tolerance, and the utilization of remote sensing technology for the early detection of pathogen attacks are highlighted.
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Abstract
Huanglongbing (HLB) and Phytophthora foot and root rot are diseases that affect citrus production and profitability. The symptoms and physiological changes associated with these diseases are diagnosed through expensive and time-consuming field measurements. Unmanned aerial vehicles (UAVs) using red/green/blue (RGB, true color) imaging, may be an economic alternative to diagnose diseases. A methodology using a UAV with a RGB camera was developed to assess citrus health. The UAV was flown in April 2018 on a grapefruit field infected with HLB and foot rot. Ten trees were selected for each of the following disease classifications: (HLB-, foot rot–), (HLB+, foot rot–), (HLB-, foot rot+) (HLB+, foot rot+). Triangular greenness index (TGI) images were correlated with field measurements such as tree nutritional status, leaf area, SPAD (leaf greenness), foot rot disease severity and HLB. It was found that 61% of the TGI differences could be explained by Na, Fe, foot rot, Ca, and K. This study shows that diseased citrus trees can be monitored using UAVs equipped with RGB cameras, and that TGI can be used to explain subtle differences in tree health caused by multiple diseases.
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