Identification of tea white star disease and anthrax based on hyperspectral image information

Lu, Bing; Sun, Jun; Yang, Ning; Wu, Xiaohong; Zhou, Xin

doi:10.1111/jfpe.13584

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…The entire sample region was selected as ROI to reduce background effects and ensure target reliability (illustrated in Figure 1b) (Lu et al., 2020). Specifically, a single band image of 816.72 nm was chosen to generate the grayscale due to the prominent spectral reflectance difference between the object and the background at this band.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging

Yao

et al. 2021

Journal of Food Science

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Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD‐RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68–1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter‐based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD‐RC‐LSSVM model obtained the best prediction accuracy for TSS, with Rp2$R_p^2$ of 0.93, with RMSEP$RMSEP$ of 0.6 %, with RER$RER$ of 18.14 and RPDp$RP{D_p}$ of 3.69. The overall results indicated that the VMD‐RC algorithm can be used as an alternative to handle high‐dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. Practical Application Traditional methods of evaluating grape quality attributes are destructive, time‐consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.

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Section: Methodsmentioning

confidence: 99%

Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging

Yao

et al. 2021

Journal of Food Science

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“…Tea leaves usually have unique surface texture characteristics, and the change in hyperspectral image information after disease can distinguish healthy tea leaves from diseased ones and determine whether they are diseased or not. Lu et al used hyperspectral images to identify white star disease and anthracnose in tea [ 96 ]. Preprocessing was first performed to select the best feature wavelengths for the spectral data using SPA.…”

Section: Application Of Spectroscopic Techniques In Tea Fresh Leaf Qu...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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“…Extreme learning machine is another machine learning algorithm used in this study. In previous studies, the ELM algorithm has been used more often to study diseases, such as tobacco (Zhu et al, 2017), tomato (Xie et al, 2015), and tea (Lu et al, 2020), but it has not been seen much in the early detection of rice leaf blast. In this study, the modeling analysis revealed that the ELM constructed by combining spectral features and texture features achieved the highest early detection accuracy (90.75% for OA and 87.30% for Kappa).…”

Section: Elasticnetmentioning

confidence: 99%

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm

et al. 2022

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Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

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Identification of tea white star disease and anthrax based on hyperspectral image information

Cited by 15 publications

References 24 publications

Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging

Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm

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