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Zhang F, Wang M, Zhang F, Xiong Y, Wang X, Ali S, Zhang Y, Fu S. Hyperspectral imaging combined with GA-SVM for maize variety identification. Food Sci Nutr 2024; 12:3177-3187. [PMID: 38726456 PMCID: PMC11077206 DOI: 10.1002/fsn3.3984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 05/12/2024] Open
Abstract
The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.
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Affiliation(s)
- Fu Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
- Collaborative Innovation Center of Advanced Manufacturing for Machinery and Equipment of Henan ProvinceLuoyangChina
| | - Mengyao Wang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Fangyuan Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Ying Xiong
- College of Agriculture/PeonHenan University of Science and TechnologyLuoyangChina
| | - Xinyue Wang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Shaukat Ali
- Wah Engineering College, University of WahWah CanttPakistan
| | - Yakun Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Sanling Fu
- School of Physical EngineeringHenan University of Science and TechnologyLuoyangChina
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Bai R, Zhou J, Wang S, Zhang Y, Nan T, Yang B, Zhang C, Yang J. Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning. Foods 2024; 13:498. [PMID: 38338633 PMCID: PMC10855119 DOI: 10.3390/foods13030498] [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: 12/18/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.
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Affiliation(s)
- Ruibin Bai
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Junhui Zhou
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Siman Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Yue Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Tiegui Nan
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Bin Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jian Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
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Zhang L, An D, Wei Y, Liu J, Wu J. Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network. Food Chem 2022; 395:133563. [PMID: 35763927 DOI: 10.1016/j.foodchem.2022.133563] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/07/2022] [Accepted: 06/21/2022] [Indexed: 11/04/2022]
Abstract
An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.
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Affiliation(s)
- Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jianwei Wu
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
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Tu K, Wen S, Cheng Y, Xu Y, Pan T, Hou H, Gu R, Wang J, Wang F, Sun Q. A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning. PLANT METHODS 2022; 18:81. [PMID: 35690826 PMCID: PMC9188178 DOI: 10.1186/s13007-022-00918-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/31/2022] [Indexed: 05/24/2023]
Abstract
BACKGROUND Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. RESULTS Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. CONCLUSIONS This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
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Affiliation(s)
- Keling Tu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Shaozhe Wen
- Beijing Key Laboratory of Vegetable Germplasm Improvement, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China
| | - Ying Cheng
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Yanan Xu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Tong Pan
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Haonan Hou
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Riliang Gu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Jianhua Wang
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Fengge Wang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China.
| | - Qun Sun
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China.
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