Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

Hu, Yating; Ma, Benxue; Wang, Huting; Zhang, Yuanjia; Li, Yujie; Yu, Guowei

doi:10.3389/fpls.2023.1105601

Cited by 18 publications

(9 citation statements)

References 52 publications

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“…The CNN used in this study consisted of the input layer, convolutional layer, batch normalization layer, average pooling layer, fully connected layer, and output layer, and its structure is shown in Figure 4 . The function of the input layer was mainly to normalize the input data, which can improve the model’s generalization ability and increase the training speed ( Hu et al., 2023 ). The convolutional layer uses convolutional operations to filter out redundant information in the original data, enhance the information related to the output, and achieve automatic feature extraction ( Wang et al., 2022 ).…”

Section: Principles and Methodsmentioning

confidence: 99%

“…The convolution kernel size was set to 3 × 3, the convolution mode was set to "same," and the step size was set to 1. The number of convolution kernels needed to be adapted to the structure of the training data, which was determined by a trial-and-error method based on the performance evaluation index of the model (Ma et al, 2023). The activation function in the neural network structure can make a nonlinear mapping of the Multi-sensor data acquisition system.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

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To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

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Section: Principles and Methodsmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

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“…(2023) harnessed the power of CNNs coupled with spectroscopy to identify the provenance of duck eggs, utilizing a dataset encompassing 261 samples. Furthermore, Hu et al. (2023) applied a one-dimensional convolutional neural network in conjunction with spectroscopy to detect pesticide residues on the Hami melon surface.…”

Section: Methodsmentioning

confidence: 99%

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Li,

Zhou,

Zhang

et al. 2024

Front. Plant Sci.

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Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

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“…The CNN used in this study consisted of the input layer, convolutional layer, batch normalization layer, average pooling layer, fully connected layer, and output layer, and its structure is shown in Figure 4. The function of the input layer was mainly to normalize the input data, which can improve the model's generalization ability and increase the training speed (Hu et al, 2023). The convolutional layer uses convolutional operations to filter out redundant information in the original data, enhance the information related to the output, and achieve automatic feature extraction .…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

Cited by 18 publications

References 52 publications

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

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