1
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Ali MA, Lyu X, Ersan MS, Xiao F. Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:135041. [PMID: 38941829 DOI: 10.1016/j.jhazmat.2024.135041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/22/2024] [Accepted: 06/25/2024] [Indexed: 06/30/2024]
Abstract
In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology's robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI's application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.
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Affiliation(s)
- Mansurat A Ali
- Department of Civil & Environmental Engineering, University of North Dakota, Grand Forks, ND 58202-8115, United States
| | - Xueyan Lyu
- School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Mahmut S Ersan
- Department of Civil & Environmental Engineering, University of North Dakota, Grand Forks, ND 58202-8115, United States
| | - Feng Xiao
- Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, United States; Missouri Water Center, University of Missouri, Columbia, MO 65211, United States.
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2
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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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3
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Krupitzer C, Stein A. Unleashing the Potential of Digitalization in the Agri-Food Chain for Integrated Food Systems. Annu Rev Food Sci Technol 2024; 15:307-328. [PMID: 37931153 DOI: 10.1146/annurev-food-012422-024649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Digitalization transforms many industries, especially manufacturing, with new concepts such as Industry 4.0 and the Industrial Internet of Things. However, information technology also has the potential to integrate and connect the various steps in the supply chain. For the food industry, the situation is ambivalent: It has a high level of automatization, but the potential of digitalization is so far not used today. In this review, we discuss current trends in information technology that have the potential to transform the food industry into an integrated food system. We show how this digital transformation can integrate various activities within the agri-food chain and support the idea of integrated food systems. Based on a future-use case, we derive the potential of digitalization to tackle future challenges in the food industry and present a research agenda.
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Affiliation(s)
- Christian Krupitzer
- Department of Food Informatics, University of Hohenheim, Stuttgart, Germany;
- Computational Science Hub, University of Hohenheim, Stuttgart, Germany
| | - Anthony Stein
- Department of Artificial Intelligence in Agricultural Engineering, University of Hohenheim, Stuttgart, Germany
- Computational Science Hub, University of Hohenheim, Stuttgart, Germany
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4
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Liang Y, Tian J, Hu X, Huang Y, He K, Xie L, Yang H, Huang D, Zhou Y, Xia Y. Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques. J Food Sci 2024; 89:3540-3553. [PMID: 38720570 DOI: 10.1111/1750-3841.17102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/21/2024] [Accepted: 04/12/2024] [Indexed: 06/14/2024]
Abstract
Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with anR P 2 $\ R_P^2$ of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with anR P 2 $R_P^2$ of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.
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Affiliation(s)
- Yan Liang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Yuexiang Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Kangling He
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Liangliang Xie
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Haili Yang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Dan Huang
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Yifei Zhou
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Yuanyuan Xia
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
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5
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Chen Y, Wang Y, Zhang Y, Wang X, Zhang C, Cheng N. Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0. Foods 2024; 13:235. [PMID: 38254535 PMCID: PMC10815208 DOI: 10.3390/foods13020235] [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: 12/14/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.
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Affiliation(s)
- Yuehua Chen
- School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yicheng Wang
- School of Food Science, Northeast Agricultural University, Harbin 150030, China;
| | - Yiran Zhang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Xin Wang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Chen Zhang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Nan Cheng
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
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6
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Zhou X, Liu W, Li K, Lu D, Su Y, Ju Y, Fang Y, Yang J. Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy. Foods 2023; 12:4371. [PMID: 38231878 DOI: 10.3390/foods12234371] [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: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024] Open
Abstract
Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.
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Affiliation(s)
- Xuejian Zhou
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Wenzheng Liu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Kai Li
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Dongqing Lu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yuan Su
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yanlun Ju
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yulin Fang
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Jihong Yang
- College of Enology, Northwest A&F University, Yangling 712100, China
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China
- Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100, China
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7
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Yu Z, Chen X, Zhang J, Su Q, Wang K, Liu W. Rapid and Non-Destructive Estimation of Moisture Content in Caragana Korshinskii Pellet Feed Using Hyperspectral Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:7592. [PMID: 37688047 PMCID: PMC10490800 DOI: 10.3390/s23177592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5-2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.
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Affiliation(s)
| | | | - Jianchao Zhang
- College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; (Z.Y.); (X.C.); (Q.S.); (K.W.); (W.L.)
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8
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Förster E, Cumme S, Kraus M, Dobschal HJ, Hillmer H, Brunner R. Catadioptric sensor concept with interlaced beam paths for imaging and pinpoint spectroscopy. APPLIED OPTICS 2023; 62:5170-5178. [PMID: 37707220 DOI: 10.1364/ao.492506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/13/2023] [Indexed: 09/15/2023]
Abstract
This paper presents the concept, optical design, and implementation of a catadioptric sensor for simultaneous imaging of a scene and pinpoint spectroscopy of a selected position, with object distances ranging from tens of centimeters to infinity and from narrow to wide adjustable viewing angles. The use of reflective imaging elements allows the implementation of folded and interlaced beam paths for spectroscopy and image acquisition, which enables a compact setup with a footprint of approximately 90m m×80m m. Although the wavelength range addressed extends far beyond the visible spectrum and reaches into the near infrared (∼400n m to 1000 nm), only three spherical surfaces are needed to project the intermediate image onto the image detector. The anamorphic imaging introduced by the folded beam path with different magnification factors in the horizontal and vertical directions as well as distortion can be compensated by software-based image processing. The area of the scene to be spectrally analyzed is imaged onto the input of an integrated miniature spectrometer. The imaging properties and spectroscopic characteristics are demonstrated in scenarios close to potential applications such as product sorting and fruit quality control.
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9
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Ping F, Yang J, Zhou X, Su Y, Ju Y, Fang Y, Bai X, Liu W. Quality Assessment and Ripeness Prediction of Table Grapes Using Visible-Near-Infrared Spectroscopy. Foods 2023; 12:2364. [PMID: 37372575 DOI: 10.3390/foods12122364] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (a*) and Chroma (C*) and soluble solids (SSC) content and decreasing values in color of lightness (L*), yellowness/blueness (b*) and Hue angle (h*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.
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Affiliation(s)
- Fengjiao Ping
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Jihong Yang
- College of Enology, Northwest A&F University, Yangling 712100, China
- Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100, China
| | - Xuejian Zhou
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yuan Su
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yanlun Ju
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yulin Fang
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Xuebing Bai
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Wenzheng Liu
- College of Enology, Northwest A&F University, Yangling 712100, China
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10
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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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11
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Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249790. [PMID: 36560157 PMCID: PMC9784550 DOI: 10.3390/s22249790] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 06/13/2023]
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
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Affiliation(s)
- Rong Cui
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - He Yu
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Tingfa Xu
- Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Xiaoxue Xing
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Xiaorui Cao
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Kang Yan
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Jiexi Chen
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
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12
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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13
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Luo J, Forsberg E, He S. 5D-fusion imaging for surface shape, polarization, and hyperspectral measurement. APPLIED OPTICS 2022; 61:7776-7785. [PMID: 36256380 DOI: 10.1364/ao.467484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
We present a five-dimensional (5D) imager that is capable of simultaneous detection of the surface shape, spectral characteristics, and polarization states of macroscopic objects, and straightforwardly fuse collected data into a 5D data set. A polarized module that uses a polarized camera obtains polarized images, while a 3D hyperspectral module reconstructs the target as a 3D point cloud using a fringe projection technique. A liquid-crystal tunable filter is placed in front of the camera of this module to acquire spectral data that can be assigned to corresponding point clouds directly. The two modules are coupled by a dual-path configuration that allows the polarization information to be merged into a comprehensive point cloud with spectral information, generating a new 5D model. The 5D imager shows excellent performance, with a spectral resolution of 10 nm, depth accuracy of 30.7 µm, and imaging time of 8 s. Sample experiments on a toy car with micro scratch defects and a yellowing plant are presented to demonstrate the capabilities of the 5D imager and its potential for use in a broad range of applications, such as industrial manufacturing inspection, plant health monitoring, and biological analysis.
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14
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Bu Y, Jiang X, Tian J, Hu X, Fei X, Huang D, Luo H. Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Youhua Bu
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xinna Jiang
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Jianping Tian
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xinjun Hu
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xue Fei
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Dan Huang
- College of Bioengineering Sichuan University of Science and Engineering Yibin China
- Sichuan Engineering Technology Research Center for Liquor‐Making Grains Yibin China
| | - Huibo Luo
- College of Bioengineering Sichuan University of Science and Engineering Yibin China
- Sichuan Engineering Technology Research Center for Liquor‐Making Grains Yibin China
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15
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Wang J, Sun L, Feng G, Bai H, Yang J, Gai Z, Zhao Z, Zhang G. Intelligent detection of hard seeds of snap bean based on hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 275:121169. [PMID: 35358780 DOI: 10.1016/j.saa.2022.121169] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/14/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.
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Affiliation(s)
- Jiaying Wang
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Laijun Sun
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Guojun Feng
- College of Modern Agriculture and Ecological Environment (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Hongyi Bai
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Jun Yang
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Zhaodong Gai
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Zhide Zhao
- School of Electronic Engineering (Heilongjiang University), Harbin, Heilongjiang, China.
| | - Guanghui Zhang
- College of Modern Agriculture and Ecological Environment (Heilongjiang University), Harbin, Heilongjiang, China.
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16
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Luo J, Forsberg E, Fu S, He S. High-precision four-dimensional hyperspectral imager integrating fluorescence spectral detection and 3D surface shape measurement. APPLIED OPTICS 2022; 61:2542-2551. [PMID: 35471321 DOI: 10.1364/ao.449529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
A four-dimensional hyperspectral imager (FDHI) that combines fluorescence spectral detection and 3D surface morphology measurement is proposed. The FDHI consists of a hyperspectral line-scanner, a line structured light stereo vision system, and a line laser. The line laser is used as both the excitation light for the fluorescence and the scanning light line for the 3D profiling. At each scanning step, the system collects both fluorescent and 3D spatial data of the irradiated line region, which are fused to 4D data points based on a line mapping relationship between the datasets, and by scanning across the measurement object, a complete 4D dataset is obtained. The FDHI shows excellent performance with spatial and spectral resolution of 26.0 µm and 3 nm, respectively. The reported FDHI system and its applications provide a solution for 4D detection and analysis of fluorescent objects in meters measurement range, with advantage of high integration as two imaging modules sharing a same laser source.
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17
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Gu H, Huang X, Chen Q, Sun Y, Lv R. A feasibility study for rapid evaluation of emulsion oxidation using synchronous fluorescence spectroscopy coupled with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120337. [PMID: 34530201 DOI: 10.1016/j.saa.2021.120337] [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: 06/16/2021] [Revised: 07/19/2021] [Accepted: 08/25/2021] [Indexed: 06/13/2023]
Abstract
A rapid method based on three-dimensional synchronous fluorescence spectroscopy was developed for emulsion oxidation evaluation. This method was selected because of its high sensitivity to dissolved organic matter typically occurring in the lipid oxidation. Spectral signal and chemical reference measurements were recorded for each emulsion sample as input and output data for the model construction. Characteristic values were extracted from the spectral data by the application of parallel factor (PARAFAC) analysis. Partial least squares regression (PLSR) was then used to construct a regression model for the rapid determination of emulsion oxidation. The correlation coefficient of the calibration and prediction sets were used as the performance parameters for the PLSR models as follows: R = 0.929, 0.973 for emulsion samples stored at 25℃; R = 0.897, 0.903 for emulsion samples stored at 70℃. The overall results demonstrated that the fluorescence spectroscopy, coupled with PARAFAC and PLSR algorithms, could be successfully used as a rapid method for the emulsion oxidation evaluation.
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Affiliation(s)
- Haiyang Gu
- School of Bio and Food Engineering, Chuzhou University, Chuzhou 239000, China.
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- School of Bio and Food Engineering, Chuzhou University, Chuzhou 239000, China; School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yanhui Sun
- School of Bio and Food Engineering, Chuzhou University, Chuzhou 239000, China
| | - Riqin Lv
- School of Bio and Food Engineering, Chuzhou University, Chuzhou 239000, China
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18
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Wei Y, Hu W, Wu F, He Y. Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN. RSC Adv 2021; 12:1141-1148. [PMID: 35425102 PMCID: PMC8978902 DOI: 10.1039/d1ra08577h] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 12/16/2021] [Indexed: 11/21/2022] Open
Abstract
This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology. In order to determine the MN and DP concentrations nondestructively, we built radial basis function neural network (RBFNN) models based on NIR spectra (874-1734 nm) with a novel chemometric method to calculate the radial bases. And excellent results with the R P 2 coefficients of 0.906 and 0.913 were obtained by the MN and DP detection models, respectively. In order to simplify the detection models based on full-range spectra, we designed an innovative genetic algorithm-successive projections algorithm (GA-SPA) strategy to extract the feature bands efficiently in two stages. Based on the feature bands selected by GA-SPA, we established the simplified detection models with the same high performance as those based on full-range spectra. By importing the feature bands of every pixel in the hyperspectral image into the simplified detection models, we successfully generated the distribution maps of MN and DP. Moreover, we also built an RBFNN classifier to categorize the habitats of Dendrobium. And the total classification accuracy reached 0.887. This research makes progress in Dendrobium quality evaluation and spectral detection technology.
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Affiliation(s)
- Yuzhen Wei
- School of Information Engineering, Huzhou University Huzhou Zhejiang China.,Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources Huzhou Zhejiang China
| | - Wenjun Hu
- School of Information Engineering, Huzhou University Huzhou Zhejiang China.,Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources Huzhou Zhejiang China
| | - Feiyue Wu
- School of Materials and Chemical Engineering, Chongqing University of Arts and Sciences Chongqing China
| | - Yi He
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University Lin'an Hangzhou China
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19
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Dong C, Yang C, Liu Z, Zhang R, Yan P, An T, Zhao Y, Li Y. Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging. SENSORS 2021; 21:s21238051. [PMID: 34884054 PMCID: PMC8659440 DOI: 10.3390/s21238051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/16/2021] [Accepted: 11/21/2021] [Indexed: 12/04/2022]
Abstract
Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.
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Affiliation(s)
- Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Chongshan Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Zhongyuan Liu
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Rentian Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Peng Yan
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Ting An
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Yan Zhao
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
- Correspondence: (Y.Z.); (Y.L.)
| | - Yang Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- Correspondence: (Y.Z.); (Y.L.)
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20
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Zhang N, Li PC, Liu H, Huang TC, Liu H, Kong Y, Dong ZC, Yuan YH, Zhao LL, Li JH. Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter. PLANT METHODS 2021; 17:117. [PMID: 34774082 PMCID: PMC8590316 DOI: 10.1186/s13007-021-00815-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 10/26/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. RESULTS We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. CONCLUSIONS Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.
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Affiliation(s)
- Ning Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Peng-Cheng Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Hubin Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Tian-Cheng Huang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Han Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Yu Kong
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Zhi-Cheng Dong
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Yu-Hui Yuan
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Long-Lian Zhao
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Jun-Hui Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
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21
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Luo J, Zhang H, Forsberg E, Hou S, Li S, Xu Z, Chen X, Sun X, He S. Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae. OPTICS EXPRESS 2021; 29:37281-37301. [PMID: 34808804 DOI: 10.1364/oe.438253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/11/2021] [Indexed: 06/13/2023]
Abstract
We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.
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22
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Luo J, Li S, Forsberg E, He S. 4D surface shape measurement system with high spectral resolution and great depth accuracy. OPTICS EXPRESS 2021; 29:13048-13070. [PMID: 33985049 DOI: 10.1364/oe.423755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/06/2021] [Indexed: 06/12/2023]
Abstract
A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems' image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.
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