Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique

Wang, Huting; Hu, Rong; Zhang, Mengyun; Zhai, Zhiqiang; Zhang, Ruoyu

doi:10.1111/jfpe.13654

Cited by 24 publications

(9 citation statements)

References 36 publications

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“…Gao Z. et al [16] used HSI to detect grape leaf roll disease and showed that HSI technology has great potential in the non-destructive detection of virus infection in grape plants during the asymptomatic period. In the study of Wang H. et al [17], HSI was used to detect tomatoes with early decay, showing that 100% of rotten tomatoes and 97.5% of healthy tomatoes could be identified by HSI. The study of Hu N. et al [18] showed that HSI could well predict the trace element content of wheat including Ca, Mg, Mo and Zn.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Plasma-Treated Rice Based on 3D Deep Residual Network with Attention Mechanism

et al. 2023

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Low-temperature plasma is a new agricultural green technology, which can improve the yield and quality of rice. How to identify the harvest rice grown by plasma seed treatment plays an important role in the popularization and application of low-temperature plasma in agriculture. This study collected hyperspectral data of harvest rice, including plasma seed treated rice, and constructed a recognition model based on the hyperspectral image (HSI) by 3D ResNet (HSI-3DResNet), which extracts spatial spectral features of HSI data cubes through 3D convolution. In addition, a spectral channels 3D attention module (C3DAM) is proposed, which can extract key features of spectra. Experiments showed that the proposed C3DAM can improve the recognition accuracy of the model to 4.2%, while the size and parameters of the model only increase by 4.1% and 3.8%, respectively. The HSI-3DResNet proposed in this study is superior to other methods with the overall accuracy of 97.47%. At the same time, the algorithm proposed in this paper was also verified on a public dataset.

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Section: Introductionmentioning

confidence: 99%

Recognition of Plasma-Treated Rice Based on 3D Deep Residual Network with Attention Mechanism

et al. 2023

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“…Siedliska et al 11 processed the raw hyperspectral reflectance spectral data with second‐order derivatives and selected 19 feature variables to build a back‐propagation neural network discriminant model, for fungal infection of strawberry fruit, with a classification accuracy of 97%. Wang et al 12 used the visible‐near‐infrared (Vis‐NIR) hyperspectral reflectance imaging coupled with image‐spectrum merging processing and analysis technology to detect early decay on tomatoes, with a correct classification rate of 97.5% for decayed fruit. All the above studies focused on the specific damage in fruit, and lacked the detection of various defects in fruit.…”

Section: Introductionmentioning

confidence: 99%

Detection of skin defects in loquats based on grayscale features combined with reflectance, absorbance, and Kubelka–Munk spectra

Han

Wang

et al. 2022

Journal of Chemometrics

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Skin defects are one of the main problems that occur during postharvest grading and processing of loquat, leading to deterioration of loquat, reducing the economic value of the commodity, and causing food quality and safety problems. Improving the identification rate of skin defects in loquat can reduce the economic loss caused by transportation and storage. In this paper, hyperspectral imaging technology was used to collect the reflectance (R), absorbance (A), and Kubelka-Munk (KM) spectra of loquat with skin defects for classification of defects types. Principal component analysis (PCA) was used to obtain the characteristic wavelength images. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variables elimination (UVE), and Monte Carlo combined with uninformative variables elimination (MCUVE) were used to reduce the dimension of spectral data to obtain the characteristic wavelength. The spectral data and grayscale features were used to establish the spectral model (SPEC) and grayscale features combined with spectral model (MIX). Extreme learning machine (ELM), least squares support vector machine (LS-SVM), and k-nearest neighbors (KNN) algorithm were applied to establish a classification model for skin defects in loquat. Comparing the model classification results of the three spectral parameters combined image features, it was found that the A-CARS-MIX-ELM model had the highest accuracy, with a classification accuracy of 98.18%. The number of selected characteristic spectra was 37, accounting for 21.02% of the total spectral number of the whole band. In the online detection process of a large number of fruits, we usually need to improve the detection speed on the premise of high-precision detection. In this case, the R-SPA-MIX-ELM model can be selected, and the classification accuracy is 94.55%. The number of selected spectra is 10, accounting for 5.68% of the number of wavelengths in the whole band. Consequently, it also provides a theoretical reference for the rapid, nondestructive, and high-precision fruit online detection technology in the future.

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“…Currently, the main extraction and calculation methods of factors affecting grasping prioritization include infrared image analysis, spectrum analysis, laser inspection and machine vision [12,13]. Compared with other methods, machine vision, which possesses the advantages of non−contact, powerful adaptability and high cost performance, is more suitable for grasping prioritization evaluation of stacked fruit clusters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Comprehensive Evaluation for Grasping Prioritization of Stacked Fruit Clusters Based on Relative Hierarchy Factor Set

2022

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To solve the problems of unclear boundaries and inconsistent influence weights among prioritization evaluation factors for grasping stacked fruit clusters by parallel robots, a fuzzy comprehensive evaluation method for the grasping prioritization of stacked fruit clusters based on a relative hierarchy factor set is proposed. According to the morphological features of stacked fruit clusters and motion features of parallel robots, a hierarchical tree model without a cross based on a subtree structure is constructed to analyze the multiple factors with unclear boundaries. A relative factor set with positive and negative effects is constructed, and a mathematical expectation is used to construct an average random consistency index and consistency satisfaction value for improving the consistency of influence weights and precision of consistency verification for a comparison matrix. The weight vector is constructed from the top to the bottom of the model, and the membership matrix of the multi−layer factors and grasping prioritization are calculated from bottom to top. The results showed that the average precision of grasping prioritization of stacked fruit clusters based on the proposed method increased by 27.77% compared with the existing fuzzy comprehensive evaluation method. The proposed method can effectively improve prioritization precision for grasping randomly stacked fruit clusters affected by multiple factors and can further realize accurate automatic sorting.

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Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique

Cited by 24 publications

References 36 publications

Recognition of Plasma-Treated Rice Based on 3D Deep Residual Network with Attention Mechanism

Recognition of Plasma-Treated Rice Based on 3D Deep Residual Network with Attention Mechanism

Detection of skin defects in loquats based on grayscale features combined with reflectance, absorbance, and Kubelka–Munk spectra

Fuzzy Comprehensive Evaluation for Grasping Prioritization of Stacked Fruit Clusters Based on Relative Hierarchy Factor Set

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