Visualization research of moisture content in leaf lettuce leaves based on WT‐PLSR and hyperspectral imaging technology

Zhou, Xin; Sun, Jun; Mao, Hanping; Wu, Xiaohong; Xiao-dong, Zhang; Yang, Ning

doi:10.1111/jfpe.12647

Cited by 43 publications

(33 citation statements)

References 20 publications

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“…Therefore, 185 spectral bands from 475 to 941 nm were selected for further analysis. In general, the spectral data contain random noise generated by the effects of operation, instrument or environment (Zhou et al, ). Therefore, it is necessary to preprocess the spectral data before selecting optimal wavelengths and building the model.…”

Section: Methodsmentioning

confidence: 99%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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In this study, the feasibility of the fluorescence hyperspectral imaging (FHSI) technology to detect the viability of soybean seeds was investigated. Viable and nonviable seed samples were obtained by artificial aging method. Hyperspectral images of samples were collected by the FHSI device and then the spectral data were collected. Characteristic wavelengths were respectively selected by three variable selection methods, eliminating a large number of redundant information irrelevant to the viability of soybean seeds. Support vector machine (SVM) models based on the full spectra and the optimal spectral data were developed to identify the viability of soybean seeds. To further improve the accuracy of the model, the adaptive boosting (AdaBoost) algorithm was used. The results showed that the accuracy of the calibration and validation sets in the CARS‐SVM‐AdaBoost model (22 characteristic wavelengths) reached 100%, indicating that the combination of FHSI technology and the optimization model can greatly improve the recognition accuracy. Practical applications A rapid and accurate nondestructive identification method of viability of soybean seeds can contribute to the construction of the online seed viability detection system. FHSI technology has the advantages of high sensitivity and comprehensive analysis of sample information. Combined with the optimization model proposed in this paper, the recognition accuracy can be greatly improved. It can be applied to the online seed viability detection by seed companies, seed quality inspection departments, and soybean breeding units.

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Section: Methodsmentioning

confidence: 99%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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“…One of the main uses is the measuring of the canopy temperature distribution within trees [11]. Another application is for testing the moisture content in leaves of lettuce samples [12]. In this regard, Hernández-Hernández et al [13] proposed a method for choosing the optimal color space for plant segmentation in the agricultural domain.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Sabzi

Abbaspour‐Gilandeh

Garcı́a-Mateos

et al. 2018

Water

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Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

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“…Wavelet transform (WT) can describe the local characteristics of the signal time (space) and frequency (scale) domains, and transform the original spectral data into the wavelet domain by wavelet transform. Through the analysis of the larger difference band (maximum singular value) in the detailed value (high frequency component) of the signal, the approximate value (low frequency component) of the signal under the optimal decomposition layer is obtained, and the noise is removed (Zhou et al, ).…”

Section: Methodsmentioning

confidence: 99%

Spectral classification of lettuce cadmium stress based on information fusion and VISSA‐GOA‐SVM algorithm

Zhou

Sun

Tian

et al. 2019

J Food Process Engineering

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In order to study the qualitative Vis-NIR spectra detection of cadmium stress in lettuce leaves, this article uses data-level information fusion method coupled with VISSA-GOA-SVM algorithm for research and analysis. There were 140 pieces of lettuce leaves under four gradients of cadmium stress, as well as a total of 560 samples of lettuce were used to collect Vis-NIR spectral information. Wavelet transform (WT), first derivative (1st Der) and second derivative (2nd Der) were used to process original Vis-NIR spectral data, respectively. Besides, the data after different pretreatments will be fused in the data-decision level and used as a new input layer. Principal component analysis (PCA), iteratively retaining informative variables (IRIV), and variable iterative space shrinkage approach (VISSA) were used to reduce the dimensionality of the data layer, respectively.Besides, support vector machine (SVM) was used to establish a classification model. The results showed that the RTD (initial fusion of three data input layers) combined with VISSA-GOA-SVM (VISSA combined with grasshopper optimization support vector machine) classification model was the best, and the accuracy of the calibration and prediction were 100% and 98.57%, respectively. Application of Vis-NIR hyperspectral imaging technique to detect cadmium stress gradient in lettuce leaves provided a new method for identifying different heavy metal residues in lettuce. Practical applicationsIt is of great significance to detect different cadmium stress levels through nondestructive testing. In order to effectively implement the rapid and nondestructive testing of lettuce leaves under different cadmium stress levels, Vis-NIR hyperspectral imaging technology coupled with data-level fusion method was used in this article. In addition, VISSA-GOA-SVM model was established to detect cadmium stress level. It confirms that the Vis-NIR hyperspectral imaging technology is a feasible and effective method for discriminating different cadmium stress levels.

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Visualization research of moisture content in leaf lettuce leaves based on WT‐PLSR and hyperspectral imaging technology

Cited by 43 publications

References 20 publications

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Spectral classification of lettuce cadmium stress based on information fusion and VISSA‐GOA‐SVM algorithm

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