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Kang YS, Ryu CS, Kang JG. Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato Seedlings Using Hyperspectral Imaging. SENSORS (BASEL, SWITZERLAND) 2024; 24:4260. [PMID: 39001041 PMCID: PMC11244052 DOI: 10.3390/s24134260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 06/29/2024] [Accepted: 06/29/2024] [Indexed: 07/16/2024]
Abstract
Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of 10 nm, 25 nm, and 50 nm using a commercialized bandpass filter. Using the permutation importance method and regression coefficients, we developed the least absolute shrinkage and selection operator (Lasso) regression models by setting the band number to ≥11, ≤10, and ≤5 for each FWHM. The regression model using 56 bands with an FWHM of 5 nm resulted in an R2 of 0.71, an RMSE of 3.99 mg/g, and an RE of 9.04%, whereas the model developed using the spectral data of only 5 bands with a FWHM of 25 nm (at 519.5 nm, 620.1 nm, 660.3 nm, 719.8 nm, and 980.3 nm) provided an R2 of 0.62, an RMSE of 4.54 mg/g, and an RE of 10.3%. These results show that a multispectral image sensor can be developed to predict the total polyphenol content of tomato seedlings subjected to low-temperature stress, paving the way for energy saving and low-temperature stress damage prevention in vegetable seedling production.
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Affiliation(s)
- Ye Seong Kang
- Department of Smart Agro-Industry, Institute of Agriculture and Life Sciences, Gyeongsang National University, Jinju 52725, Republic of Korea;
| | - Chan Seok Ryu
- Department of Biosystem Engineering, Institute of Agriculture and Life Sciences, Gyeongsang National University, Jinju 52828, Republic of Korea
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Yang S, Cao Y, Li C, Castagnini JM, Barba FJ, Shan C, Zhou J. Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis. Curr Res Food Sci 2024; 8:100695. [PMID: 38362161 PMCID: PMC10867766 DOI: 10.1016/j.crfs.2024.100695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/13/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024] Open
Abstract
This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods.
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Affiliation(s)
- Sicheng Yang
- Huanggang Public Testing Center, No.128 Huangzhou Avenue, Huanggang City, Hubei Province, China
| | - Yang Cao
- Academy of State Administration of Grain, Beijing, 100037, China
| | - Chuanjie Li
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing, 163319, Heilongjiang, China
| | - Juan Manuel Castagnini
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Francisco Jose Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Changyao Shan
- College of Science, Health, Engineering and Education, Murdoch University, Perth, 6150, Australia
| | - Jianjun Zhou
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
- Department of Biotechnology, Institute of Agrochemistry and Food Technology-National Re-search Council (IATA-CSIC), Agustin Escardino 7, 46980, Paterna, Spain
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Ye S, Liu W, Zeng S, Wu G, Chen L, Lai H, Yan Z. SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure. SENSORS (BASEL, SWITZERLAND) 2023; 23:6194. [PMID: 37448042 PMCID: PMC10346631 DOI: 10.3390/s23136194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
During the rice quality testing process, the precise segmentation and extraction of grain pixels is a key technique for accurately determining the quality of each seed. Due to the similar physical characteristics, small particles and dense distributions of rice seeds, properly analysing rice is a difficult problem in the field of target segmentation. In this paper, a network called SY-net, which consists of a feature extractor module, a feature pyramid fusion module, a prediction head module and a prototype mask generation module, is proposed for rice seed instance segmentation. In the feature extraction module, a transformer backbone is used to improve the ability of the network to learn rice seed features; in the pyramid fusion module and the prediction head module, a six-layer feature fusion network and a parallel prediction head structure are employed to enhance the utilization of feature information; and in the prototype mask generation module, a large feature map is used to generate high-quality masks. Training and testing were performed on two public datasets and one private rice seed dataset. The results showed that SY-net achieved a mean average precision (mAP) of 90.71% for the private rice seed dataset and an average precision (AP) of 16.5% with small targets in COCO2017. The network improved the efficiency of rice seed segmentation and showed excellent application prospects in performing rice seed quality testing.
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Affiliation(s)
- Sheng Ye
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; (S.Y.); (L.C.); (H.L.); (Z.Y.)
| | - Weihua Liu
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; (S.Y.); (L.C.); (H.L.); (Z.Y.)
| | - Shan Zeng
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China;
| | - Guiju Wu
- The Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430023, China;
| | - Liangyan Chen
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; (S.Y.); (L.C.); (H.L.); (Z.Y.)
| | - Huaqing Lai
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; (S.Y.); (L.C.); (H.L.); (Z.Y.)
| | - Zi Yan
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; (S.Y.); (L.C.); (H.L.); (Z.Y.)
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Xu P, Sun W, Xu K, Zhang Y, Tan Q, Qing Y, Yang R. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning. Foods 2022; 12:foods12010144. [PMID: 36613360 PMCID: PMC9818215 DOI: 10.3390/foods12010144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.
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Affiliation(s)
- Peng Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Wenbin Sun
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Kang Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Yunpeng Zhang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Qian Tan
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Yiren Qing
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Ranbing Yang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
- Correspondence: ; Tel.: +86-0898-66267576
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Du H. Forensic Characterization of Tires by Attenuated Total Reflectance–Fourier Transform Infrared (ATR–FTIR) Spectroscopy and Machine Learning Algorithms. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2138422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Haojun Du
- Department of Traffic Management, Hunan Police College, Changsha, Hunan, China
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Kang Z, Huang T, Zeng S, Li H, Dong L, Zhang C. A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:5333. [PMID: 35891015 PMCID: PMC9315692 DOI: 10.3390/s22145333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
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Affiliation(s)
| | | | - Shan Zeng
- Correspondence: ; Tel.: +86-153-2728-6970
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Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning. Foods 2022; 11:foods11111609. [PMID: 35681359 PMCID: PMC9180647 DOI: 10.3390/foods11111609] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 12/25/2022] Open
Abstract
Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.
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Method for Data Quality Assessment of Synthetic Industrial Data. SENSORS 2022; 22:s22041608. [PMID: 35214509 PMCID: PMC8876977 DOI: 10.3390/s22041608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 11/16/2022]
Abstract
Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.
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