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Lu Y, Nie L, Guo X, Pan T, Chen R, Liu X, Li X, Li T, Liu F. Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116704. [PMID: 38996646 DOI: 10.1016/j.ecoenv.2024.116704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/14/2024] [Accepted: 07/06/2024] [Indexed: 07/14/2024]
Abstract
Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.
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Affiliation(s)
- Yi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Linjie Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xinyu Guo
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xunyue Liu
- College of Advanced Agricultural Sciences, Zhejiang A & F University, Hangzhou 311300, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Tingqiang Li
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Zhai Y, Zhou L, Qi H, Gao P, Zhang C. Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0124. [PMID: 38239738 PMCID: PMC10795768 DOI: 10.34133/plantphenomics.0124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/17/2023] [Indexed: 01/22/2024]
Abstract
Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.
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Affiliation(s)
- Yuanning Zhai
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
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3
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Khan IU, Zhang YF, Shi XN, Qi SS, Zhang HY, Du DL, Gul F, Wang JH, Naz M, Shah SWA, Jia H, Li J, Dai ZC. Dose dependent effect of nitrogen on the phyto extractability of Cd in metal contaminated soil using Wedelia trilobata. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 264:115419. [PMID: 37651793 DOI: 10.1016/j.ecoenv.2023.115419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 08/23/2023] [Accepted: 08/27/2023] [Indexed: 09/02/2023]
Abstract
Cadmium (Cd) is one of the toxic heavy metal that negatively affect plant growth and compromise food safety for human consumption. Nitrogen (N) is an essential macronutrient for plant growth and development. It may enhance Cd tolerance of invasive plant species by maintaining biochemical and physiological characteristics during phytoextraction of Cd. A comparative study was conducted to evaluate the phenotypical and physiological responses of invasive W. trilobata and native W. chinensis under low Cd (10 µM) and high Cd (80 µM) stress, along with different N levels (i.e., normal 91.05 mg kg-1 and low 0.9105 mg kg-1). Under low-N and Cd stress, the growth of leaves, stem and roots in W. trilobata was significantly increased by 35-23%, 25-28%, and 35-35%, respectively, compared to W. chinensis. Wedelia trilobata exhibited heightened antioxidant activities of catalase and peroxidase were significantly increased under Cd stress to alleviate oxidative stress. Similarly, flavonoid content was significantly increased by 40-50% in W. trilobata to promote Cd tolerance via activation of the secondary metabolites. An adverse effect of Cd in the leaves of W. chinensis was further verified by a novel hyperspectral imaging technology in the form of normalized differential vegetation index (NDVI) and photochemical reflectance index (PRI) compared to W. trilobata. Additionally, W. trilobata increased the Cd tolerance by regulating Cd accumulation in the shoots and roots, bolstering its potential for phytoextraction potential. This study demonstrated that W. trilobata positively responds to Cd with enhanced growth and antioxidant capabilities, providing a new platform for phytoremediation in agricultural lands to protect the environment from heavy metals pollution.
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Affiliation(s)
- Irfan Ullah Khan
- School of Emergency Management, Jiangsu University, Zhenjiang 212013, China; Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yi-Fan Zhang
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Xin-Ning Shi
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Shan-Shan Qi
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hai-Yan Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; School of Inspection and Testing Certification, Changzhou Vocational Institute of Engineering, Changzhou 213164, China
| | - Dao-Lin Du
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Farrukh Gul
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jia-Hao Wang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Misbah Naz
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Syed Waqas Ali Shah
- Biofuels Institute, School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hui Jia
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jian Li
- Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Zhi-Cong Dai
- School of Emergency Management, Jiangsu University, Zhenjiang 212013, China; Institute of Environment and Ecology, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China; Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu Province, China.
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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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Zhou X, Zhao C, Sun J, Yao K, Xu M. Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122288. [PMID: 36608517 DOI: 10.1016/j.saa.2022.122288] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/22/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
The evaluation capability of hyperspectral imaging technology was studied for the forecasts of heavy metal lead concentration of oilseed rape plant. In addition, a transfer stacked auto-encoder (T-SAE) algorithm including two network methods, the dual-model T-SAE and the single-model T-SAE, was proposed in this paper. The hyperspectral images of oilseed rape leaf and root were acquired under different Pb stress concentrations. The entire region of the oilseed rape leaf (or root) was selected as the region of interest (ROI) to extract the spectral data, and standard normalized variable (SNV), first derivative (1st Der) and second derivative (2nd Der) were used to preprocess the ROI spectra. Besides, the principal component analysis (PCA) algorithm was used to reduce the dimensionality of the spectral data before and after preprocessing. Hence, the best pre-processed data was determined for subsequent research and analysis. Furthermore, the SAE deep learning networks were built based on the oilseed rape leaf data, oilseed rape root data, and the combined data of oilseed rape leaf and root based on the best pre-processed spectral data. Finally, the T-SAE models were obtained through transfer learning of the best SAE deep learning network. The results show that the best preprocessing algorithms of the oilseed rape leaf and root spectra were SNV and 1st Der algorithm, respectively. In addition, the prediction set recognition accuracy of the best T-SAE model of Pb stress gradient in oilseed rape plants was 98.75%. Additionally, the prediction set coefficient of determination of the best T-SAE model of the Pb content in the oilseed rape leaf and root data were 0.9215 and 0.9349, respectively. Therefore, a deep transfer learning method combined with hyperspectral imaging technology can effectively realize the the qualitative and quantitative detection of heavy metal Pb in oilseed rape plants.
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Affiliation(s)
- Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Chunjiang Zhao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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Cheng J, Sun J, Yao K, Xu M, Wang S, Fu L. Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:2690-2699. [PMID: 36479694 DOI: 10.1002/jsfa.12376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/21/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. RESULTS Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg-1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. CONCLUSION The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Simin Wang
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Lvhui Fu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, 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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Lay L, Lee HS, Tayade R, Ghimire A, Chung YS, Yoon Y, Kim Y. Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging. PLANTS (BASEL, SWITZERLAND) 2023; 12:901. [PMID: 36840248 PMCID: PMC9967622 DOI: 10.3390/plants12040901] [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: 12/30/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.
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Affiliation(s)
- Liny Lay
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hong Seok Lee
- Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Republic of Korea
| | - Rupesh Tayade
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
- Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Amit Ghimire
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea
| | - Youngnam Yoon
- Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Republic of Korea
| | - Yoonha Kim
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
- Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea
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Qi YP, He PJ, Lan DY, Xian HY, Lü F, Zhang H. Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 153:20-30. [PMID: 36041267 DOI: 10.1016/j.wasman.2022.08.014] [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: 04/03/2022] [Revised: 07/13/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.
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Affiliation(s)
- Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China.
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10
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Li J, Ren J, Cui R, Yu K, Zhao Y. Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review. FRONTIERS IN PLANT SCIENCE 2022; 13:1007991. [PMID: 36352874 PMCID: PMC9638174 DOI: 10.3389/fpls.2022.1007991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/05/2022] [Indexed: 05/26/2023]
Abstract
Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.
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Affiliation(s)
- Junmeng Li
- College of Mechanical and Electronic Engineering, Northwest A &F University, Yangling, China
| | - Jie Ren
- College of Mechanical and Electronic Engineering, Northwest A &F University, Yangling, China
| | - Ruiyan Cui
- College of Mechanical and Electronic Engineering, Northwest A &F University, Yangling, China
| | - Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A &F University, Yangling, China
- Key Lab Agricultural Internet Things, Ministry of Agriculture & Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A &F University, Yangling, China
- Key Lab Agricultural Internet Things, Ministry of Agriculture & Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
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Cheng J, Sun J, Yao K, Xu M, Wang S, Fu L. Development of multi-disturbance bagging Extreme Learning Machine method for cadmium content prediction of rape leaf using hyperspectral imaging technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121479. [PMID: 35696971 DOI: 10.1016/j.saa.2022.121479] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/19/2022] [Accepted: 06/04/2022] [Indexed: 06/15/2023]
Abstract
Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp2 of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.
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Affiliation(s)
- Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Simin Wang
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Lvhui Fu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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12
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Tao M, He Y, Bai X, Chen X, Wei Y, Peng C, Feng X. Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification. FRONTIERS IN PLANT SCIENCE 2022; 13:973745. [PMID: 36003818 PMCID: PMC9393615 DOI: 10.3389/fpls.2022.973745] [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: 06/21/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.
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Affiliation(s)
- Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiaoyun Chen
- Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuzhen Wei
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cheng Peng
- Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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13
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Attaallah R, Amine A. Highly selective and sensitive detection of cadmium ions by horseradish peroxidase enzyme inhibition using a colorimetric microplate reader and smartphone paper-based analytical device. Microchem J 2022. [DOI: 10.1016/j.microc.2021.106940] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Zea M, Souza A, Yang Y, Lee L, Nemali K, Hoagland L. Leveraging high-throughput hyperspectral imaging technology to detect cadmium stress in two leafy green crops and accelerate soil remediation efforts. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 292:118405. [PMID: 34710518 DOI: 10.1016/j.envpol.2021.118405] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/17/2021] [Accepted: 10/23/2021] [Indexed: 06/13/2023]
Abstract
Cadmium (Cd) is a toxic metal that can accumulate in soils and negatively impact crop as well as human health. Amendments like biochar have potential to address these challenges by reducing Cd bioavailability in soil, though reliance on post-harvest wet chemical methods to quantify Cd uptake have slowed efforts to identify the most effective amendments. Hyperspectral imaging (HSI) is a novel technology that could overcome this limitation by quantifying symptoms of Cd stress while plants are still growing. The goals of this study were to: 1) determine whether HSI can detect Cd stress in two distinct leafy green crops, 2) quantify whether a locally sourced biochar derived from hardwoods can reduce Cd stress and uptake in these crops, and 3) identify vegetative indices (VIs) that best quantify changes in plant stress responses. Experiments were conducted in a tightly controlled automated phenotyping facility that allowed all environmental factors to be kept constant except Cd concentration (0, 5 10 and 15 mg kg-1). Symptoms of Cd stress were stronger in basil (Ocimum basilicum) than kale (Brassica oleracea), and were easier to detect using HSI. Several VIs detected Cd stress in basil, but only the anthocyanin reflectance index (ARI) detected all levels of Cd stress in both crop species. The biochar amendment did reduce Cd uptake, especially at low Cd concentrations in kale which took up more Cd than basil. Again, the ARI index was the most effective in quantifying changes in plant stress mediated by the biochar. These results indicate that the biochar evaluated in this study has potential to reduce Cd bioavailability in soil, and HSI could be further developed to identify rates that can best achieve this benefit. The technology also may be helping in elucidating mechanisms mediating how biochar can influence plant growth and stress responses.
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Affiliation(s)
- Maria Zea
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA
| | - Augusto Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Linda Lee
- Department of Agronomy, Purdue University, West Lafayette, IN, 47907, USA
| | - Krishna Nemali
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA
| | - Lori Hoagland
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA.
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15
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Zhou X, Sun J, Zhang Y, Tian Y, Yao K, Xu M. Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible‐near infrared hyperspectral imaging. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Yuechun Zhang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Yan Tian
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Min Xu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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16
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Zhao Y, Gouda M, Yu G, Zhang C, Lin L, Nie P, Huang W, Ye H, Ye Y, Zhou C, He Y. Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 225:112800. [PMID: 34547661 DOI: 10.1016/j.ecoenv.2021.112800] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/13/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC2), Cd-PC2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC2 chelates Cd2+ in a 2:1 ratio to form Cd(PC2)2 complex; Cd connected to thoils of PC2 and changed β-turn and random coil of PC2 peptide chain to β-Sheet which presented as terahertz vibrations of PC2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.
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Affiliation(s)
- Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, Egypt
| | - Guohong Yu
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chenghao Zhang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Lei Lin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Pengcheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Wei Huang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Hongbao Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yunxiang Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chengquan Zhou
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
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17
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Yu K, Fang S, Zhao Y. Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118917. [PMID: 32949945 DOI: 10.1016/j.saa.2020.118917] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 08/16/2020] [Accepted: 09/01/2020] [Indexed: 06/11/2023]
Abstract
Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400-1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L-1 (non-stressed groups), 1, 3, and 5 mg·L-1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg.
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Affiliation(s)
- Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China
| | - Shiyan Fang
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China.
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Zhu H, Chen L, Xing W, Ran S, Wei Z, Amee M, Wassie M, Niu H, Tang D, Sun J, Du D, Yao J, Hou H, Chen K, Sun J. Phytohormones-induced senescence efficiently promotes the transport of cadmium from roots into shoots of plants: A novel strategy for strengthening of phytoremediation. JOURNAL OF HAZARDOUS MATERIALS 2020; 388:122080. [PMID: 31954299 DOI: 10.1016/j.jhazmat.2020.122080] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/11/2020] [Accepted: 01/12/2020] [Indexed: 05/24/2023]
Abstract
Due to the long growth period of plants, phytoremediation is time costly. Improving the accumulation of cadmium (Cd) in shoots of plants will promote the efficiency of phytoremediation. In this study, two senescence-relative phytohormones, abscisic acid (ABA) and salicylic acid (SA), were applied to strengthening phytoremediation of Cd by tall fescue (Festuca arundinacea S.). Under hydroponic culture, phytohormones treatment increased the Cd content of shoots 11.4-fold over the control, reaching 316.3 mg/kg (dry weight). Phytohormones-induced senescence contributes to the transport of heavy metals, and HMA3 was found to play a key role in this process. Additionally, this strategy could strengthen the accumulation of Cu and Zn in tall fescue shoots. Moreover, in soil pot culture, the strategy increased shoot Cd contents 2.56-fold over the control in tall fescue, and 2.55-fold over the control in Indian mustard (Brassica juncea L.), indicating its comprehensive adaptability and potential use in the field. In summary, senescence-induced heavy metal transport is developed as a novel strategy to strengthen phytoremediation. The strategy could be applied at the end of phytoremediation with an additional short duration (7 days) with comprehensive adaptability, and markedly strengthen the phytoremediation in the field.
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Affiliation(s)
- Huihui Zhu
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China; CAS Key Laboratory of Aquatic Botany and Watershed Ecology & CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China
| | - Liang Chen
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology & CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China
| | - Wei Xing
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology & CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China
| | - Shangmin Ran
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Zhihui Wei
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Maurice Amee
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology & CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China
| | - Misganaw Wassie
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology & CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China
| | - Hong Niu
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Diyong Tang
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Jie Sun
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Dongyun Du
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China
| | - Jun Yao
- School of Water Resources & Environment, China University of Geosciences Beijing, Beijing, PR China
| | - Haobo Hou
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, PR China
| | - Ke Chen
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China.
| | - Jie Sun
- College of Resources and Environmental Science, Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science, South-Central University for Nationalities, Wuhan, PR China.
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