Diagnosing the symptoms of sheath blight disease on rice stalk with an in-situ hyperspectral imaging technique

Zhang, Jingcheng; Tian, Yangyang; Yan, Lijie; Wang, Bin; Xu, Junfeng; Wu, Kaihua

doi:10.1016/j.biosystemseng.2021.06.020

Cited by 21 publications

(8 citation statements)

References 19 publications

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“…Spectral information covers wavelengths from 450 to 950 nm and is characterized by a high dimension of redundancy between adjacent wavelengths. Excessive redundant spectral information brings great challenges to detection methods and computational complexity ( Hennessy et al, 2020 ; Zhang et al, 2021b ). Therefore, it is necessary to compress the amount of data by a dimensionality reduction method to reduce the cost of subsequent processing on the basis of not dropping the effective feature spectral information ( Poona et al, 2016 ; Sadeghi-Tehran et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Cao

Yuan

et al. 2022

Front. Plant Sci.

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Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky–Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.

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Section: Methodsmentioning

confidence: 99%

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Cao

Yuan

et al. 2022

Front. Plant Sci.

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“…The non-plant background removal is important in disease detection with hyperspectral imaging data since it permits the subsequent analysis focusing only on the plant leaves. In detecting rice sheath blight disease with an in-situ hyperspectral imaging data, a more complex strategy was used combining K-means clustering and spectral feature space analysis [29]. While considering the relatively simple experimental environment, the high-quality background removal results can be achieved with this simple method, and the 750 nm band can also be used in subsequent steps.…”

Section: Leaf Abnormal Area Identificationmentioning

confidence: 99%

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Yuan¹,

Zhang²,

Deng³

et al. 2023

Phyton

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Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

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“…Since plants and background regions in spectral images have significant differences in the near-infrared and red bands, Zhang et al [73] removed the non-plant background using the K-means clustering algorithm before the detection of the scabs of rice sheath blight. Compared to the traditional background removal methods based on pixel threshold segmentation, the clustering-based method can mitigate the issue of irregular edges and the "salt and pepper" phenomenon.…”

Section: Machine Learningmentioning

confidence: 99%

Hyperspectral Sensing of Plant Diseases: Principle and Methods

Wan

et al. 2022

Agronomy

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Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

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Diagnosing the symptoms of sheath blight disease on rice stalk with an in-situ hyperspectral imaging technique

Cited by 21 publications

References 19 publications

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral Sensing of Plant Diseases: Principle and Methods

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