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Pugh NA, Young A, Ojha M, Emendack Y, Sanchez J, Xin Z, Puppala N. Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms. FRONTIERS IN PLANT SCIENCE 2024; 15:1339864. [PMID: 38444530 PMCID: PMC10912196 DOI: 10.3389/fpls.2024.1339864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.
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Affiliation(s)
- N. Ace Pugh
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Andrew Young
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Manisha Ojha
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
| | - Yves Emendack
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Jacobo Sanchez
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Zhanguo Xin
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
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Janni M, Maestri E, Gullì M, Marmiroli M, Marmiroli N. Plant responses to climate change, how global warming may impact on food security: a critical review. FRONTIERS IN PLANT SCIENCE 2024; 14:1297569. [PMID: 38250438 PMCID: PMC10796516 DOI: 10.3389/fpls.2023.1297569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Global agricultural production must double by 2050 to meet the demands of an increasing world human population but this challenge is further exacerbated by climate change. Environmental stress, heat, and drought are key drivers in food security and strongly impacts on crop productivity. Moreover, global warming is threatening the survival of many species including those which we rely on for food production, forcing migration of cultivation areas with further impoverishing of the environment and of the genetic variability of crop species with fall out effects on food security. This review considers the relationship of climatic changes and their bearing on sustainability of natural and agricultural ecosystems, as well as the role of omics-technologies, genomics, proteomics, metabolomics, phenomics and ionomics. The use of resource saving technologies such as precision agriculture and new fertilization technologies are discussed with a focus on their use in breeding plants with higher tolerance and adaptability and as mitigation tools for global warming and climate changes. Nevertheless, plants are exposed to multiple stresses. This study lays the basis for the proposition of a novel research paradigm which is referred to a holistic approach and that went beyond the exclusive concept of crop yield, but that included sustainability, socio-economic impacts of production, commercialization, and agroecosystem management.
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Affiliation(s)
- Michela Janni
- Institute of Bioscience and Bioresources (IBBR), National Research Council (CNR), Bari, Italy
- Institute of Materials for Electronics and Magnetism (IMEM), National Research Council (CNR), Parma, Italy
| | - Elena Maestri
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Mariolina Gullì
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Marta Marmiroli
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Nelson Marmiroli
- Consorzio Interuniversitario Nazionale per le Scienze Ambientali (CINSA) Interuniversity Consortium for Environmental Sciences, Parma/Venice, Italy
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Vurro F, Croci M, Impollonia G, Marchetti E, Gracia-Romero A, Bettelli M, Araus JL, Amaducci S, Janni M. Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato. PLANTS (BASEL, SWITZERLAND) 2023; 12:3851. [PMID: 38005747 PMCID: PMC10674827 DOI: 10.3390/plants12223851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 11/26/2023]
Abstract
Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red-green-blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop's status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.
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Affiliation(s)
- Filippo Vurro
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - Michele Croci
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Giorgio Impollonia
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Edoardo Marchetti
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Agrotecnio—Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain; (A.G.-R.); (J.L.A.)
- Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain
| | - Manuele Bettelli
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Agrotecnio—Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain; (A.G.-R.); (J.L.A.)
| | - Stefano Amaducci
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Michela Janni
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
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Shi R, Seiler C, Knoch D, Junker A, Altmann T. Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought. FRONTIERS IN PLANT SCIENCE 2023; 14:1233553. [PMID: 37719228 PMCID: PMC10502302 DOI: 10.3389/fpls.2023.1233553] [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: 06/02/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023]
Abstract
In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.
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Affiliation(s)
- Rongli Shi
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Christiane Seiler
- Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute (JKI), Quedlinburg, Germany
| | - Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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