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Lin Y, Gao J, Tu Y, Zhang Y, Gao J. Estimating low concentration heavy metals in water through hyperspectral analysis and genetic algorithm-partial least squares regression. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170225. [PMID: 38246365 DOI: 10.1016/j.scitotenv.2024.170225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Hyperspectral spectrum enables assessment of heavy metal content, but research on low concentration in water is limited. This study employed in situ hyperspectral data from Dalian Lake, Shanghai to develop a machine learning model for accurately determining heavy metal concentrations. Initially, we employed a combination of empirical analysis and algorithm-based analysis to identify the optimal features for retrieving Cu and Fe ions. Based on the correlation coefficients between heavy metals and water quality, the feature bands for TOC, Chl-a and TP were selected as empirical features. Algorithm-based feature selection was conducted by employing the random forest (RF) approach with the original spectrum (OR), first-order derivative reflectance (FDR), and second-order derivative reflectance (SDR). For the development of a prediction model, we utilized the Genetic Algorithm-Partial Least Squares Regression (GA-PLSR) approach for Cu and Fe ions inversion. Our findings demonstrated that the integration of both empirical features and algorithm-selected features resulted in superior performance compared to using algorithm-selected features alone. Importantly, the crucial wavelength data primarily located at 497, 665, 686, 831 and 935 nm showed superior results for Cu retrieval, while wavelengths of 700, 746, 801, 948, and 993 nm demonstrated better results for Fe retrieval. These results also displayed that the GA-PLSR model outperformed both the PLSR and RF models, exhibiting an R2 of 0.75, RMSE of 0.004, and MRE of 0.382 for Cu inversion. For Fe inversion, the GA-PLSR model outperformed other models with an R2 of 0.73, RMSE of 0.036, and MRE of 0.464. This research provides a scientific basis and data support for monitoring low concentrations of heavy metals in water bodies using hyperspectral remote sensing techniques.
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Affiliation(s)
- Yukun Lin
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China
| | - Jiaxin Gao
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Yaojen Tu
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China.
| | - Yuxun Zhang
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jun Gao
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China
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Chedid E, Avia K, Dumas V, Ley L, Reibel N, Butterlin G, Soma M, Lopez-Lozano R, Baret F, Merdinoglu D, Duchêne É. LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0116. [PMID: 38026470 PMCID: PMC10655830 DOI: 10.34133/plantphenomics.0116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations (R2) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
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Affiliation(s)
- Elsa Chedid
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Komlan Avia
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Vincent Dumas
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Lionel Ley
- INRAE, UEAV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Nicolas Reibel
- INRAE, UEAV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Gisèle Butterlin
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Maxime Soma
- INRAE, Aix-Marseille Université, UMR RECOVER, 3275 Route de Cézanne, 13182 Aix-en-Provence, France
| | - Raul Lopez-Lozano
- INRAE,
Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome, 84914 Avignon, France
| | - Frédéric Baret
- INRAE,
Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome, 84914 Avignon, France
| | - Didier Merdinoglu
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
| | - Éric Duchêne
- INRAE,
University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France
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Fernández-Novales J, Barrio I, Diago MP. Towards the automation of NIR spectroscopy to assess vineyard water status spatial-temporal variability from a ground moving vehicle. Sci Rep 2023; 13:13362. [PMID: 37591887 PMCID: PMC10435444 DOI: 10.1038/s41598-023-39039-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/19/2023] [Indexed: 08/19/2023] Open
Abstract
Irrigation has a strong impact in terms of yield regulation and grape and wine quality, so the implementation of precision watering systems would facilitate the decision-making process about the water use efficiency and the irrigation scheduling in viticulture. The objectives of this work were two-fold. On one hand, to compare and assess grapevine water status using two different spectral devices assembled in a mobile platform and to evaluate their capability to map the spatial variability of the plant water status in two commercial vineyards from July to early October in season 2021, and secondly to develop an algorithm capable of automate the spectral acquisition process using one of the two spectral sensors previously tested. Contemporarily to the spectral measurements collected from the ground vehicle at solar noon, stem water potential (Ψs) was used as the reference method to evaluate the grapevine water status. Calibration and prediction models for grapevine water status assessment were performed using the Partial least squares (PLS) regression and the Variable Importance in the Projection (VIP) method. The best regression models returned a determination coefficient for cross validation (R2cv) and external validation (R2p) of 0.70 and 0.75 respectively, and the standard error of cross validation (RMSECV) values were lower than 0.105 MPa and 0.128 MPa for Tempranillo and Graciano varieties using a more expensive and heavier near-infrared (NIR) spectrometer (spectral range 1200-2100 nm). Remarkable models were also built with the miniaturized, low-cost spectral sensor (operating between 900-1860 nm) ranging from 0.69 to 0.71 for R2cv, around 0.74 in both varieties for R2p and the RMSECV values were below 0.157 MPa, while the RMSEP values did not exceed 0.151 MPa in both commercial vineyards. This work also includes the development of a software which automates data acquisition and allows faster (up to 40% of time saving in the field) and more efficient deployment of the developed algorithm. The encouraging results presented in this work demonstrate the great potential of this methodology to assess the water status of the vineyard and estimate its spatial variability in different commercial vineyards, providing useful information for better irrigation scheduling.
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Affiliation(s)
- Juan Fernández-Novales
- Department of Agriculture and Food Science, University of La Rioja, 26007, Logroño, La Rioja, Spain.
- Institute of Grapevine and Wine Sciences, University of La Rioja, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja, 26007, Logroño, La Rioja, Spain.
| | - Ignacio Barrio
- Department of Agriculture and Food Science, University of La Rioja, 26007, Logroño, La Rioja, Spain
- Institute of Grapevine and Wine Sciences, University of La Rioja, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja, 26007, Logroño, La Rioja, Spain
| | - María Paz Diago
- Department of Agriculture and Food Science, University of La Rioja, 26007, Logroño, La Rioja, Spain.
- Institute of Grapevine and Wine Sciences, University of La Rioja, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja, 26007, Logroño, La Rioja, Spain.
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