1
|
Zhang X, Wang P, Wang Y, Hu L, Luo X, Mao H, Shen B. Cucumber powdery mildew detection method based on hyperspectra-terahertz. FRONTIERS IN PLANT SCIENCE 2022; 13:1035731. [PMID: 36247642 PMCID: PMC9558005 DOI: 10.3389/fpls.2022.1035731] [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: 09/03/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.
Collapse
Affiliation(s)
- Xiaodong Zhang
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China
| | - Pei Wang
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China
| | - Yafei Wang
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China
| | - Lian Hu
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China
| | - Xiwen Luo
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China
| | - Hanping Mao
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China
| | - Baoguo Shen
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China
| |
Collapse
|
2
|
Long J, Yang J, Peng J, Pan L, Tu K. Detection of moisture and carotenoid content in carrot slices during hot air drying based on multispectral imaging equipment with selected wavelengths. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2021. [DOI: 10.1515/ijfe-2021-0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Moisture content and carotenoid content are important indicators for evaluating the drying process of carrot slices. There are growing attention to develop non-destructive methods as effectively analytical tools in quality assurance of drying carrot slices. In this study, the characteristic wavelengths of moisture and carotenoid content in carrot slices during hot air drying were extracted based on hyperspectral imaging technology. A multispectral imaging equipment was built after that, and the wavelengths of filters were determined according to the characteristic wavelengths. Based on the successive projection algorithm (SPA), the optimal wavelengths of moisture and carotenoid content were further determined, and prediction models of both were established based on the system. There were 12 filters selected in this study. The results showed that a support vector machine (SVM) prediction model for moisture content was established based on seven optimal wavelengths with 0.991 for the coefficient of determination of prediction set (R
2
p
) and 10.318 for the residual prediction residual (RPD). Based on eight optimal wavelengths, a SVM prediction model for carotenoid content was also established with 0.968 for R
2
p
and 5.337 for RPD. The prediction performance is close to or even better than that based on hyperspectral. The study confirmed the feasibility of using the multispectral imaging equipment to measure the moisture and carotenoid content of carrot slices during drying based on selected wavelengths, laying a foundation for the further preparation of a portable multispectral detector for the quality of dry products.
Collapse
Affiliation(s)
- Jiamei Long
- College of Food Science and Technology , Nanjing Agricultural University , No. 1 Weigang Road , Nanjing , Jiangsu , 210095 , P. R. China
| | - Jia Yang
- College of Food Science and Technology , Nanjing Agricultural University , No. 1 Weigang Road , Nanjing , Jiangsu , 210095 , P. R. China
| | - Jing Peng
- College of Food Science and Technology , Nanjing Agricultural University , No. 1 Weigang Road , Nanjing , Jiangsu , 210095 , P. R. China
| | - Leiqing Pan
- College of Food Science and Technology , Nanjing Agricultural University , No. 1 Weigang Road , Nanjing , Jiangsu , 210095 , P. R. China
| | - Kang Tu
- College of Food Science and Technology , Nanjing Agricultural University , No. 1 Weigang Road , Nanjing , Jiangsu , 210095 , P. R. China
| |
Collapse
|
3
|
Özdoğan G, Lin X, Sun DW. Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.044] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
4
|
Sendin K, Manley M, Marini F, Williams PJ. Hierarchical classification pathway for white maize, defect and foreign material classification using spectral imaging. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
5
|
Cui H, Cheng Z, Li P, Miao A. Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4744. [PMID: 32842673 PMCID: PMC7506873 DOI: 10.3390/s20174744] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/16/2020] [Accepted: 08/20/2020] [Indexed: 11/17/2022]
Abstract
Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400-1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.
Collapse
Affiliation(s)
- Huawei Cui
- College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Zhishang Cheng
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Peng Li
- Department of Electronic Engineering, School of Information, Yunnan University, Kunming 650091, China;
| | - Aimin Miao
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| |
Collapse
|
6
|
Feng L, Zhu S, Liu F, He Y, Bao Y, Zhang C. Hyperspectral imaging for seed quality and safety inspection: a review. PLANT METHODS 2019; 15:91. [PMID: 31406499 PMCID: PMC6686453 DOI: 10.1186/s13007-019-0476-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 08/01/2019] [Indexed: 05/22/2023]
Abstract
Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.
Collapse
Affiliation(s)
- Lei Feng
- 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
| | - Susu Zhu
- 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
| | - 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
| | - Yong He
- 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
| | - Yidan Bao
- 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
| | - Chu Zhang
- 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
| |
Collapse
|
7
|
Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging. Molecules 2018; 23:molecules23123078. [PMID: 30477266 PMCID: PMC6321087 DOI: 10.3390/molecules23123078] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 11/21/2018] [Accepted: 11/23/2018] [Indexed: 11/17/2022] Open
Abstract
Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
Collapse
|
8
|
Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8040513] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
9
|
Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0165-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|