Detection of anthracnose in tea plants based on hyperspectral imaging

Yuan, Lin; Yan, Peng; Han, Weihong; Huang, Yanbo; Wang, Bin; Zhang, Jingcheng; Zhang, Haibo; Bao, Zhiyan

doi:10.1016/j.compag.2019.105039

Cited by 65 publications

(27 citation statements)

References 34 publications

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“…Single or multistep thresholding algorithms are commonly used for obtaining the ROI and extracting the average spectrum of a sample ( Sharma and Bhavya, 2020 ). Building prediction models based on the extracted spectra is commonly used for indoor applications ( Mishra et al, 2017 ; Yuan et al, 2019 ). Spatial variation in spectra requires more attention in pixel-wise prediction and outdoor applications for achieving high prediction accuracy ( Vaughn et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

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Section: Introductionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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“…Abdulridha et al [108] used hyper-spectral imaging technology combined with the MLP classification method, had an accuracy of 99% for the four stages of tomato bacterial spot disease and bacterial target spot disease (healthy asymptomatic stage, early stage, and late-stage). Yuan et al [109] proposed a method for detecting tea tree anthracnose based on hyperspectral imaging.…”

Section: Hyper-spectral Imaging(hsi) With DL Modelsmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

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432

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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“…Results showed that the accuracy for predicting the front and back water content reached 0.9500 and 0.9560, respectively, and the RMSEPs (root-mean-square errors of prediction) were 0.028 and 0.027, respectively. Lin Yuan et al [ 8 ] regarded 542, 686, and 754 nm as the sensitive bands for anthracnose in tea plants, based on the hyperspectral data of tea leaves, and they implemented a strategy of unsupervised classification and an adaptive two-dimensional threshold for disease recognition. The overall recognition rate reached 98%.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

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Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

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Detection of anthracnose in tea plants based on hyperspectral imaging

Cited by 65 publications

References 34 publications

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Plant Disease Detection and Classification by Deep Learning—A Review

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

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