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Liu F, Yang R, Chen R, Lamine Guindo M, He Y, Zhou J, Lu X, Chen M, Yang Y, Kong W. Digital techniques and trends for seed phenotyping using optical sensors. J Adv Res 2024; 63:1-16. [PMID: 37956859 DOI: 10.1016/j.jare.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
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Affiliation(s)
- Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jun Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
| | - Xiangyu Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mengyuan Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yinhui Yang
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
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Shi T, Gao Y, Song J, Ao M, Hu X, Yang W, Chen W, Liu Y, Feng H. Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains. Food Chem 2024; 461:140651. [PMID: 39154465 DOI: 10.1016/j.foodchem.2024.140651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/02/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024]
Abstract
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.
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Affiliation(s)
- Taotao Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Yuan Gao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Min Ao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Yanyan Liu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China.
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3
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Sun J, Nirere A, Dusabe KD, Yuhao Z, Adrien G. Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms. J Food Sci 2024; 89:4403-4418. [PMID: 38957090 DOI: 10.1111/1750-3841.17151] [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: 09/14/2023] [Revised: 04/28/2024] [Accepted: 05/15/2024] [Indexed: 07/04/2024]
Abstract
The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.
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Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Adria Nirere
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Keza Dominique Dusabe
- School of Food Science and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Zhong Yuhao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Guverinoma Adrien
- School of Food Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
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4
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Busov ID, Genaev MA, Komyshev EG, Koval VS, Zykova TE, Glagoleva AY, Afonnikov DA. A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example. Vavilovskii Zhurnal Genet Selektsii 2024; 28:443-455. [PMID: 39040972 PMCID: PMC11260993 DOI: 10.18699/vjgb-24-50] [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: 07/23/2023] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 07/24/2024] Open
Abstract
Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.
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Affiliation(s)
- I D Busov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - M A Genaev
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - E G Komyshev
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - V S Koval
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - T E Zykova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - A Y Glagoleva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - D A Afonnikov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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Jeong SW, Lyu JI, Jeong H, Baek J, Moon JK, Lee C, Choi MG, Kim KH, Park YI. SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits. PLANT CELL REPORTS 2024; 43:164. [PMID: 38852113 PMCID: PMC11162974 DOI: 10.1007/s00299-024-03249-0] [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: 04/03/2024] [Accepted: 05/06/2024] [Indexed: 06/10/2024]
Abstract
KEY MESSAGE Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.
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Affiliation(s)
- Seok Won Jeong
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea
| | - Jae Il Lyu
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - HwangWeon Jeong
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jung-Kyung Moon
- Crop Foundation Research Division, National Institute of Crop Sciences, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Chaewon Lee
- Crop Cultivation and Environment Research Division, National Institute of Crop Sciences, 54 Seohoro, Suwon, Kyounggi-do, 16613, Korea
| | - Myoung-Goo Choi
- Wheat Research Team, National Institute of Crop Sciences, RDA, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Kyoung-Hwan Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Youn-Il Park
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea.
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6
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Zhang T, Lu L, Song Y, Yang M, Li J, Yuan J, Lin Y, Shi X, Li M, Yuan X, Zhang Z, Zeng R, Song Y, Gu L. Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques. FRONTIERS IN PLANT SCIENCE 2024; 14:1342970. [PMID: 38288409 PMCID: PMC10822997 DOI: 10.3389/fpls.2023.1342970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024]
Abstract
The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.
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Affiliation(s)
- Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Long Lu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yihu Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Minyu Yang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jing Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jiduan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Yuquan Lin
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Xingren Shi
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Mingjie Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Xiaotan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Zhongyi Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Rensen Zeng
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yuanyuan Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Li Gu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
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7
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Sukhanova M, Khasanov E, Butenko A, Fayzrakhmanov S, Fayzullin R. Damage to seeds by screw working bodies. Heliyon 2023; 9:e18973. [PMID: 37609427 PMCID: PMC10440526 DOI: 10.1016/j.heliyon.2023.e18973] [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: 03/17/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023] Open
Abstract
Seed damage caused by screw working bodies of agricultural machines reduces the seed quality and increases the total cost of crop production. This paper describes the impact interaction of a particle with screw flights when seeds are fed into the transportation zone. There are velocity dependences during the impact and reflection of seeds on the screw surface at the maximum distance of the radius from the screw rotation axis. The potential energy lost by seeds when interacting with screw flights is determined. The paper considers the way transporting the seed material by screw working bodies impacts seed damage. The potential energy accumulated after impact by the reflecting surface and the particle depends on the magnitude and direction of the falling velocity to the screw belt, the auger geometric and kinematic parameters (the screw diameter; the inclination angle of the helix to the screw axis and the angular velocity), as well as the physical and mechanical properties of the particle and the reflecting surface (recovery and friction coefficients). Previous studies have shown that the critical angular velocity of the screw flight depends on the screw casing and the reduction factor of the circumferential velocity. In turn, the reduction factor depends on the transported seed material and the geometric parameters of the screw. It is possible to diminish the destruction of the seed material due to impact interaction with the working surface of the screw by reducing the inclination angle of the screw flight α, the angular speed of the screw rotation ω and its radius R. It also requires direct the seed flow closer to the rotation center. Reducing injury to seeds during transportation in a screw device requires a higher critical angular velocity of the screw. To increase the working angular velocity of the screw without exceeding its threshold of critical angular velocity, it is advisable to use screws with a large pitch and polymer screws with a wider pitch.
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Affiliation(s)
- Mayya Sukhanova
- Department of Technical Mechanics and Physics, Azov Black Sea State Engineering Institute FSBE HPE “Don State Agrarian University”, Zernograd, Russian Federation
| | - Eduard Khasanov
- Department of Agricultural and Technological Machines, Federal State Budgetary Educational Establishment of Higher Education “Bashkir State Agrarian University”, Ufa, Russian Federation
| | - Alexander Butenko
- Department of Technical Mechanics and Physics, Azov Black Sea State Engineering Institute FSBE HPE “Don State Agrarian University”, Zernograd, Russian Federation
| | - Shamil Fayzrakhmanov
- Department of Mechanics and Machine Design, Federal State Budgetary Educational Establishment of Higher Education “Bashkir State Agrarian University”, Ufa, Russian Federation
| | - Rinat Fayzullin
- Department of Agricultural and Technological Machines, Federal State Budgetary Educational Establishment of Higher Education “Bashkir State Agrarian University”, Ufa, Russian Federation
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8
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Cai Z, Huang Z, He M, Li C, Qi H, Peng J, Zhou F, Zhang C. Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches. Food Chem 2023; 422:136169. [PMID: 37119596 DOI: 10.1016/j.foodchem.2023.136169] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 04/04/2023] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.
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Affiliation(s)
- Zeyi Cai
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Zihong Huang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Mengyu He
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fei Zhou
- College of Standardization, China Jiliang University, Hangzhou 310018, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China.
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9
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Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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10
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Xu Y, Wu W, Chen Y, Zhang T, Tu K, Hao Y, Cao H, Dong X, Sun Q. Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds. FRONTIERS IN PLANT SCIENCE 2022; 13:1031849. [PMID: 36523615 PMCID: PMC9745075 DOI: 10.3389/fpls.2022.1031849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.
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Affiliation(s)
- Yanan Xu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Weifeng Wu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yi Chen
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Keling Tu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yun Hao
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Hailu Cao
- Hengde Materia Medica (Beijing) Agricultural Technology Co., Ltd., Beijing, China
| | - Xuehui Dong
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Qun Sun
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
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11
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Gao T, Chandran AKN, Paul P, Walia H, Yu H. HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds. SENSORS (BASEL, SWITZERLAND) 2021; 21:8184. [PMID: 34960287 PMCID: PMC8703337 DOI: 10.3390/s21248184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/27/2021] [Accepted: 12/04/2021] [Indexed: 01/04/2023]
Abstract
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.
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Affiliation(s)
- Tian Gao
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Anil Kumar Nalini Chandran
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Puneet Paul
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Hongfeng Yu
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
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12
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Zhao R, An L, Song D, Li M, Qiao L, Liu N, Sun H. Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 259:119768. [PMID: 33971438 DOI: 10.1016/j.saa.2021.119768] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/21/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.
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Affiliation(s)
- Ruomei Zhao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Lulu An
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Di Song
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Minzan Li
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Lang Qiao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Ning Liu
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Hong Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
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Application of Hyperspectral Imaging in the Assessment of Drought and Salt Stress in Magneto-Primed Triticale Seeds. PLANTS 2021; 10:plants10050835. [PMID: 33919429 PMCID: PMC8143295 DOI: 10.3390/plants10050835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/12/2021] [Accepted: 04/16/2021] [Indexed: 11/29/2022]
Abstract
Hyperspectral imaging is an appropriate method to thoroughly investigate the microscopic structure of internally heterogeneous agro-food products. By using hyperspectral technology, identifying stress symptoms associated with salinity, before a human observer, is possible, and has obvious benefits. The objective of this paper was to prove the suitability of this technique for the analysis of Triticale seeds subjected to both magneto-priming and drought and salt stress conditions, in terms of image differences obtained among treatments. It is known that, on the one hand, drought and salt stress treatments have negative effects on seeds of almost all species, and on the other hand, magneto-priming enhances seed germination parameters. Thus, this study aimed to relate hyperspectral imaging values—neither positive nor negative in themselves—to the effects mentioned above. Two main conclusions were reached: Firstly, the hyperspectral application is a feasible method for exploring the Triticale structure and for making distinctions under different drought and salt stress treatments, in line with the data variability obtained. Secondly, the lower spectral reflectance in some treatments—in the 400–1000 nm segment—is the result of a great number of chemical compounds in the seed that could be related to magneto-priming.
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14
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Pang L, Wang J, Men S, Yan L, Xiao J. Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118888. [PMID: 32947159 DOI: 10.1016/j.saa.2020.118888] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/19/2020] [Accepted: 08/25/2020] [Indexed: 06/11/2023]
Abstract
In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals. The optimal band was selected for the original and pre-processed spectra which were treated by multiple scatter correction (MSC) and the Savitzky-Golay first derivative (SG 1st). Five characteristic wavelength methods were compared: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable important in projection (VIP), and random frog (RF). Partial least square-discriminant analysis (PLS-DA) and K-nearest neighbor (KNN) built the vitality estimation model based on different data sets, and GA + PLS-DA constructed the optimal model with the highest accuracy. According to the weight coefficient and reflectance of the characteristic band extracted by the GA, the reflectance curves of different levels over time were plotted. The data of 0 h was employed to establish the vitality forecast model. The forecast model had a high recognition rate, with PLS-DA exceeding 99% and KNN exceeding 85%. This indicated that hyperspectral imaging of seed germination processes could achieve non-destructive estimation of Q. variabilis seed vitality, and accurate prediction in a shorter time is feasible.
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Affiliation(s)
- Lei Pang
- School of Technology, Beijing Forestry University, Beijing 100083, China
| | - Jinghua Wang
- School of Technology, Beijing Forestry University, Beijing 100083, China
| | - Sen Men
- College of Robotics, Beijing Union University, Beijing 100020, China; Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University, Beijing 100020, China
| | - Lei Yan
- School of Technology, Beijing Forestry University, Beijing 100083, China.
| | - Jiang Xiao
- School of Technology, Beijing Forestry University, Beijing 100083, China
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15
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Zhang T, Fan S, Xiang Y, Zhang S, Wang J, Sun Q. Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 239:118488. [PMID: 32470809 DOI: 10.1016/j.saa.2020.118488] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/26/2020] [Accepted: 05/13/2020] [Indexed: 05/10/2023]
Abstract
Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082 nm) and short wave infrared (SWIR, 930-2548 nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3 days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R2 results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.
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Affiliation(s)
- Tingting Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
| | - Yingying Xiang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shujie Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Jianhua Wang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Qun Sun
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
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16
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Salazar Mercado SA, Botello Delgado EA, Quintero Caleño JD. Optimización de la prueba de tetrazolio para evaluar la viabilidad en semillas de Solanum lycopersicum L. ACTA ACUST UNITED AC 2020. [DOI: 10.21930/rcta.vol21_num3_art:1344] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
El tomate (Solanum lycopersicum) es una de las hortalizas más consumidas en el mundo con un aumento en su demanda, por lo que conocer la viabilidad de sus semillas es de suma importancia, ya que estas son la base del éxito de su producción. Debido a esto, el presente estudio tuvo como objetivo optimizar la prueba de tetrazolio determinando el pretratamiento adecuado para potenciar la prueba. El material vegetal se recolectó de cultivos establecidos en el municipio del Cáchira, departamento de Norte de Santander, Colombia. Las semillas se extrajeron de los frutos maduros y, posteriormente, se expusieron a pretratramientos con hipoclorito de sodio y agua destilada durante 10 minutos, con tres concentraciones de 2, 3, 5- cloruro trifenil tetrazolio (0,25 %, 0,15 % y 0,10 %) y distintos tiempos de exposición (6 h, 12 h y 24 h). Los datos de viabilidad obtenidos se corroboraron mediante la prueba de germinación en toallas de papel húmedas. Los resultados de viabilidad más relacionados con la prueba de germinación se obtuvieron al emplear las concentraciones de 0,25 % y 0,15 %, utilizando tanto el pretratamiento con hipoclorito de sodio como la inmersión en agua destilada.
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17
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Zhang D, Chen G, Zhang H, Jin N, Gu C, Weng S, Wang Q, Chen Y. Integration of spectroscopy and image for identifying fusarium damage in wheat kernels. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 236:118344. [PMID: 32330824 DOI: 10.1016/j.saa.2020.118344] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/26/2020] [Accepted: 04/05/2020] [Indexed: 05/20/2023]
Abstract
Hyperspectral imaging (HSI) was studied for the detection of varying degrees of damage in wheat kernels caused by Fusarium head blight (Gibberella zeae), a major disease in wheat worldwide. A total of 810 wheat kernel samples were collected from a field trial with the three levels of Fusarium infection, healthy, moderate, and severe. Hyperspectral image of the wheat kernels was acquired over a wavelength range of 400-1000 nm. The raw spectral data were pre-processed, and then the optimal wavelengths were selected using principal component analysis (PCA), successive projection algorithm (SPA) and random forest (RF). The image features were extracted based on the optimal wavelengths, and then the spectral features and image features were combined as fusion features. Support vector machine (SVM), random forest (RF) and naive Bayes (NB) were employed to build the classification models to identify the degrees of Fuasrium damage based on spectral and fusion features. The best performance was obtained by using the SPA-RF method to select the optimal wavelengths and corresponding image features, with a classification accuracy of 96.44%. The method developed from this study can provide a more effective way to identify the degrees of Fusarium damage in wheat kernels.
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Affiliation(s)
- Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Gao Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Huihui Zhang
- Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, 80526, USA
| | - Ning Jin
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong 030600, China
| | - Chunyan Gu
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Qian Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Yu Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
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18
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New Spectral Classification Index for Rapid Identification of Fusarium Infection in Wheat Kernel. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01829-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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ElMasry G, ElGamal R, Mandour N, Gou P, Al-Rejaie S, Belin E, Rousseau D. Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Res Int 2020; 131:109025. [DOI: 10.1016/j.foodres.2020.109025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/13/2020] [Accepted: 01/16/2020] [Indexed: 12/27/2022]
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20
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Liu J, Liu S, Shi T, Wang X, Chen Y, Liu F, Men H. A modified feature fusion method for distinguishing seed strains using hyperspectral data. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
AbstractPrecise classification of seeds is important for agriculture. Due to the slight physical and chemical difference between different types of wheat and high correlation between bands of images, it is easy to fall into the local optimum when selecting the characteristic band of using the spectral average only. In this paper, in order to solve this problem, a new variable fusion strategy was proposed based on successive projection algorithm and the variable importance in projection algorithm to obtain a comprehensive and representative variable feature for higher classification accuracy, within spectral mean and spectral standard deviation, so the 25 feature bands obtained are classified by support vector machine, and the classification accuracy rate reached 83.3%. It indicates that the new fusion strategy can mine the effective features of hyperspectral data better to improve the accuracy of the model and it can provide a theoretical basis for the hyperspectral classification of tiny kernels.
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Affiliation(s)
- Jingjing Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
- Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, 30602, GA, USA
- Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Simeng Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Tie Shi
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Xiaonan Wang
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - Yizhou Chen
- Department of Neurobiology and Behavior, University of California, Irvine, 92697, CA, USA
| | - Fulong Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China
| | - Hong Men
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
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21
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Qin G, Pei Z, Zhang Y, Lan K, Li Q, Li L, Yu S, Chen X. Dielectric ceramics/TiO 2/single-crystalline silicon nanomembrane heterostructure for high performance flexible thin-film transistors on plastic substrates. RSC Adv 2019; 9:35289-35296. [PMID: 35530705 PMCID: PMC9074119 DOI: 10.1039/c9ra06572e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 10/20/2019] [Indexed: 12/03/2022] Open
Abstract
A dielectric ceramics/TiO2/single-crystalline silicon nanomembrane (SiNM) heterostructure is designed and fabricated for high performance flexible thin-film transistors (TFTs). Both the dielectric ceramics (Nb2O3-Bi2O3-MgO) and TiO2 are deposited by radio frequency (RF) magnetron sputtering at room temperature, which is compatible with flexible plastic substrates. And the single-crystalline SiNM is transferred and attached to the dielectric ceramics/TiO2 layers to form the heterostructure. The experimental results demonstrate that the room temperature processed heterostructure has high quality because: (1) the Nb2O3-Bi2O3-MgO/TiO2 heterostructure has a high dielectric constant (∼76.6) and low leakage current. (2) The TiO2/single-crystalline SiNM structure has a relatively low interface trap density. (3) The band gap of the Nb2O3-Bi2O3-MgO/TiO2 heterostructure is wider than TiO2, which increases the conduction band offset between Si and TiO2, lowering the leakage current. Flexible TFTs have been fabricated with the Nb2O3-Bi2O3-MgO/TiO2/SiNM heterostructure on plastic substrates and show a current on/off ratio over 104, threshold voltage of ∼1.2 V, subthreshold swing (SS) as low as ∼0.2 V dec-1, and interface trap density of ∼1012 eV-1 cm-2. The results indicate that the dielectric ceramics/TiO2/SiNM heterostructure has great potential for high performance TFTs.
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Affiliation(s)
- Guoxuan Qin
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin 300072 P. R. China
| | - Zhihui Pei
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin 300072 P. R. China
| | - Yibo Zhang
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin 300072 P. R. China
| | - Kuibo Lan
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin 300072 P. R. China
| | - Quanning Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University Tianjin 300072 P. R. China
| | - Lingxia Li
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
| | - Shihui Yu
- School of Microelectronics, Tianjin University Tianjin 300072 P. R. China
| | - Xuejiao Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University Tianjin 300072 P. R. China
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22
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Li Y, Sun J, Wu X, Chen Q, Lu B, Dai C. Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14238] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yating Li
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Bing Lu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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23
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Li L, Xie S, Ning J, Chen Q, Zhang Z. Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:1787-1794. [PMID: 30226640 DOI: 10.1002/jsfa.9371] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 08/08/2018] [Accepted: 09/10/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shimeng Xie
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Chen Z, Jiang J, Jiang X, Fang X, Cai Z. Spectral-Spatial Feature Extraction of Hyperspectral Images Based on Propagation Filter. SENSORS 2018; 18:s18061978. [PMID: 29925817 PMCID: PMC6021978 DOI: 10.3390/s18061978] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Revised: 06/06/2018] [Accepted: 06/17/2018] [Indexed: 11/30/2022]
Abstract
Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.
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Affiliation(s)
- Zhikun Chen
- School of Computer Science, China University of Geosciences, Wuhan 430074, China.
- Beibu Gulf Big Data Resources Utilisation Lab, Qinzhou University, Qinzhou 535000, China.
- Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Qinzhou 535000, China.
| | - Junjun Jiang
- School of Computer Science, China University of Geosciences, Wuhan 430074, China.
| | - Xinwei Jiang
- School of Computer Science, China University of Geosciences, Wuhan 430074, China.
| | - Xiaoping Fang
- School of Computer Science, China University of Geosciences, Wuhan 430074, China.
| | - Zhihua Cai
- School of Computer Science, China University of Geosciences, Wuhan 430074, China.
- Beibu Gulf Big Data Resources Utilisation Lab, Qinzhou University, Qinzhou 535000, China.
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