Reflectance images of effective wavelengths from hyperspectral imaging for identification of Fusarium head blight-infected wheat kernels combined with a residual attention convolution neural network

Weng, Shizhuang; Han, Kuang-Wei; Chu, Zhaojie; Zhu, Gongqin; Liu, Cunchuan; Zhu, Zhen‐Gang; Zhang, Zixi; Zheng, Ling; Huang, Liusheng

doi:10.1016/j.compag.2021.106483

Cited by 33 publications

(11 citation statements)

References 35 publications

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“…An SR-HSI image of soybean kernels contains images of 272 wavelengths with redundant information, resulting in low processing efficiency and huge modeling cost. Thus, selecting the SR-HSI image of many significant wavelengths is essential to damage identification ( Weng et al., 2021 ). Here, candidate EWs were first selected by SVM models developed with spectral reflectance of each wavelength, and OISEW was finalized by ShuffleNet and the successive superposition of monochromatic images of each EW.…”

Section: Discussionmentioning

confidence: 99%

“…The direct use of SR-HSI images containing the images of 272 wavelengths to identify damages to soybean kernels would result in low processing efficiency and high hardware and time costs. The Images of EWs have been proved as a feasible approach to alleviate the limitations in the previous works ( Weng et al., 2021 ). The selection of EWs from the reflectance spectra was based on the performance of SVM models that describe the reflectance of wavelengths and classes of soybean kernels.…”

Section: Methodsmentioning

confidence: 99%

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Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Zheng

Zhao²,

Zhu³

et al. 2023

Front. Plant Sci.

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Identification of soybean kernel damages is significant to prevent further disoperation. Hyperspectral imaging (HSI) has shown great potential in cereal kernel identification, but its low spatial resolution leads to external feature infidelity and limits the analysis accuracy. In this study, the fusion of HSI and RGB images and improved ShuffleNet were combined to develop an identification method for soybean kernel damages. First, the HSI-RGB fusion network (HRFN) was designed based on super-resolution and spectral modification modules to process the registered HSI and RGB image pairs and generate super-resolution HSI (SR-HSI) images. ShuffleNet improved with convolution optimization and cross-stage partial architecture (ShuffleNet_COCSP) was used to build classification models with the optimal image set of effective wavelengths (OISEW) of SR-HSI images obtained by support vector machine and ShuffleNet. High-quality fusion of HSI and RGB with the obvious spatial promotion and satisfactory spectral conservation was gained by HRFN. ShuffleNet_COCSP and OISEW obtained the optimal recognition performance of ACCp=98.36%, Params=0.805 M, and FLOPs=0.097 G, outperforming other classification methods and other types of images. Overall, the proposed method provides an accurate and reliable identification of soybean kernel damages and would be extended to analysis of other quality indicators of various crop kernels.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Zheng

Zhao²,

Zhu³

et al. 2023

Front. Plant Sci.

Self Cite

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“…In addition to simulating the human brain's mechanism for extracting features in layers, the technique can extract features automatically from simple to complex, from bottom to top, and from concrete to abstract. Several researchers have successfully applied CNN to the detection of seed quality [12][13][14][15]. But, a disadvantage of CNN detection is that it requires a large amount of training data, is time-consuming, and is computationally resourceintensive.…”

Section: Introductionmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

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The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

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“…Visual analysis, canopy temperature, thermal imaging, machine vision, and spectroscopic techniques are commonly used to analyze the DS degree of plants. Visual analysis, which relies on professional and experienced inspectors, is convenient and nondestructive but susceptible to subjective interference (Weng et al, 2021). Canopy temperature and thermal imaging can quantify the complex relationship between temperature and stress degree without needing physical contact, but they are affected by the aliasing of plants and soil background information (Ni et al, 2015;Han et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Weng

Ma²,

Tao³

et al. 2023

Front. Plant Sci.

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Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson’s correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses

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Reflectance images of effective wavelengths from hyperspectral imaging for identification of Fusarium head blight-infected wheat kernels combined with a residual attention convolution neural network

Cited by 33 publications

References 35 publications

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

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