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Song Y, Sapes G, Chang S, Chowdhry R, Mejia T, Hampton A, Kucharski S, Sazzad TMS, Zhang Y, Tillman BL, Resende MFR, Koppal S, Wilson C, Gerber S, Zare A, Hammond WM. Hyperspectral signals in the soil: Plant-soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38924477 DOI: 10.1111/pce.15011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/12/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024]
Abstract
Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.
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Affiliation(s)
- Yangyang Song
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Gerard Sapes
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Spencer Chang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Ritesh Chowdhry
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Tomas Mejia
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Anna Hampton
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Shelby Kucharski
- School of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA
| | - T M Shahiar Sazzad
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Yuxuan Zhang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Barry L Tillman
- North Florida Research and Education Center, University of Florida, Marianna, Florida, USA
| | - Márcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA
| | - Sanjeev Koppal
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Chris Wilson
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Stefan Gerber
- Soil, Water and Ecosystem Sciences Department, University of Florida, Gainesville, Florida, USA
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - William M Hammond
- Agronomy Department, University of Florida, Gainesville, Florida, USA
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Luo S, Yuan X, Liang R, Feng K, Xu H, Zhao J, Wang S, Lan Y, Long Y, Deng H. Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 297:122720. [PMID: 37058840 DOI: 10.1016/j.saa.2023.122720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 05/14/2023]
Abstract
Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with Rp2 of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with Rp2 of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with Rp2 of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.
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Affiliation(s)
- Shuiyang Luo
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Xue Yuan
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
| | - Ruiqing Liang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Kunsheng Feng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haitao Xu
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Jing Zhao
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Shaokui Wang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yubin Lan
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Yongbing Long
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haidong Deng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
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Zhang W, Suo J, Dong K, Li L, Yuan X, Pei C, Dai Q. Handheld snapshot multi-spectral camera at tens-of-megapixel resolution. Nat Commun 2023; 14:5043. [PMID: 37598234 PMCID: PMC10439928 DOI: 10.1038/s41467-023-40739-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/07/2023] [Indexed: 08/21/2023] Open
Abstract
Multi-spectral imaging is a fundamental tool characterizing the constituent energy of scene radiation. However, current multi-spectral video cameras cannot scale up beyond megapixel resolution due to optical constraints and the complexity of the reconstruction algorithms. To circumvent the above issues, we propose a tens-of-megapixel handheld multi-spectral videography approach (THETA), with a proof-of-concept camera achieving 65-megapixel videography of 12 wavebands within visible light range. The high performance is brought by multiple designs: We propose an imaging scheme to fabricate a thin mask for encoding spatio-spectral data using a conventional film camera. Afterwards, a fiber optic plate is introduced for building a compact prototype supporting pixel-wise encoding with a large space-bandwidth product. Finally, a deep-network-based algorithm is adopted for large-scale multi-spectral data decoding, with the coding pattern specially designed to facilitate efficient coarse-to-fine model training. Experimentally, we demonstrate THETA's advantageous and wide applications in outdoor imaging of large macroscopic scenes.
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Affiliation(s)
- Weihang Zhang
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jinli Suo
- Department of Automation, Tsinghua University, Beijing, 100084, China.
- Institute of Brain and Cognitive Sciences, Tsinghua University, Beijing, 10008, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Kaiming Dong
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Lianglong Li
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Xin Yuan
- WestLake University, Hangzhou, 310030, Zhejiang, China
| | | | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, 100084, China.
- Institute of Brain and Cognitive Sciences, Tsinghua University, Beijing, 10008, China.
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Sanaeifar A, Yang C, de la Guardia M, Zhang W, Li X, He Y. Proximal hyperspectral sensing of abiotic stresses in plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160652. [PMID: 36470376 DOI: 10.1016/j.scitotenv.2022.160652] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Ce Yang
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, United States.
| | - Miguel de la Guardia
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain.
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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Meng G, Rasmussen SK, Christensen CSL, Fan W, Torp AM. Molecular breeding of barley for quality traits and resilience to climate change. Front Genet 2023; 13:1039996. [PMID: 36685930 PMCID: PMC9851277 DOI: 10.3389/fgene.2022.1039996] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Barley grains are a rich source of compounds, such as resistant starch, beta-glucans and anthocyanins, that can be explored in order to develop various products to support human health, while lignocellulose in straw can be optimised for feed in husbandry, bioconversion into bioethanol or as a starting material for new compounds. Existing natural variations of these compounds can be used to breed improved cultivars or integrated with a large number of mutant lines. The technical demands can be in opposition depending on barley's end use as feed or food or as a source of biofuel. For example beta-glucans are beneficial in human diets but can lead to issues in brewing and poultry feed. Barley breeders have taken action to integrate new technologies, such as induced mutations, transgenics, marker-assisted selection, genomic selection, site-directed mutagenesis and lastly machine learning, in order to improve quality traits. Although only a limited number of cultivars with new quality traits have so far reached the market, research has provided valuable knowledge and inspiration for future design and a combination of methodologies to achieve the desired traits. The changes in climate is expected to affect the quality of the harvested grain and it is already a challenge to mitigate the unpredictable seasonal and annual variations in temperature and precipitation under elevated [CO2] by breeding. This paper presents the mutants and encoded proteins, with a particular focus on anthocyanins and lignocellulose, that have been identified and characterised in detail and can provide inspiration for continued breeding to achieve desired grain and straw qualities.
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Affiliation(s)
- Geng Meng
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,College of Horticulture, Henan Agricultural University, Zhengzhou, China
| | - Søren K. Rasmussen
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,*Correspondence: Søren K. Rasmussen,
| | | | - Weiyao Fan
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Anna Maria Torp
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
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Ren Y, Zhu J, Zhang H, Lin B, Hao P, Hua S. Leaf Carbohydrate Metabolism Variation Caused by Late Planting in Rapeseed (Brassica napus L.) at Reproductive Stage. PLANTS 2022; 11:plants11131696. [PMID: 35807649 PMCID: PMC9268982 DOI: 10.3390/plants11131696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
Abstract
Delayed planting date of rapeseed is an important factor affecting seed yield. However, regulation of the leaf carbohydrate metabolism in rapeseed by a late planting date at the reproductive stage is scarcely investigated. A two-year field experiment was conducted to assess the effect of planting dates, including early (15 September), optimal (1 October), late (15 October), and very late (30 October), on leaf growth and carbohydrate biosynthetic and catabolic metabolism at the reproductive stage. The results showed that leaf dry matter decreased linearly on average from 7.48 to 0.62 g plant−1 with an early planting date, whereas it increased at first and peaked at 14 days after anthesis (DAA) with other planting dates. Leaf dry matter was the lowest at the very late planting date during the reproductive stage. For leaf chlorophyll content, rapeseed planted at an optimal date maximized at 14 DAA with an average content of 1.51 mg g−1 fresh weight, whereas it kept high and stable at a very late planting date after 28 DAA. For the carbohydrate catabolic system, acid and neutral invertase (AI and NI, respectively) showed higher activity before 14 DAA, whereas both sucrose synthase (SS) and starch phosphorylase (SP) showed higher activity after 14 DAA. For the carbohydrate biosynthetic system, the activity of sucrose phosphate synthase (SPS) was the highest at the late planting date after 14 DAA, whereas it was at the lowest at the very late planting date. However, the activity of ADP-glucose pyrophosphorylase (AGPase) at the late and very late planting dates was significantly higher than that of the early and optimal plant dates after 21 DAA, which is in accordance with the leaf total soluble sugar content, suggesting that leaf carbohydrate metabolism is governed by a biosynthetic system. The current study provides new insights on leaf carbohydrate metabolism regulation by late planting in rapeseed at the reproductive stage.
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Affiliation(s)
- Yun Ren
- Huzhou Agricultural Science and Technology Development Center, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China; (Y.R.); (J.Z.)
| | - Jianfang Zhu
- Huzhou Agricultural Science and Technology Development Center, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China; (Y.R.); (J.Z.)
| | - Hui Zhang
- Zhejiang Agro-Tech Extension and Service Center, Hangzhou 310020, China;
| | - Baogang Lin
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
| | - Pengfei Hao
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
| | - Shuijin Hua
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
- Correspondence:
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