Inhibitory effect of CaCl<sub>2</sub> and carboxymethyl chitosan coating on the after‐ripening of Korla fragrant pears in cold storage

Wang, Zhentao; Tang, Yurong; Liu, Yang; Zhai, Hong; Zhang, Yongcheng; Lan, Haipeng

doi:10.1111/ijfs.15339

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…To illustrate the variations in spectral reflectance of rice caused by different nutrient stressors and how the spectral reflectance of stress samples and NPK are related, PCA of spectral reflectance of rice stress samples from various spectral regions can be extracted and use PCA to analyze the spectral variety of rice under various nutrient stresses [ 41 , 42 ]. The variation in the rice stress samples’ spectrum reflectance across different spectral regions is indicated by the amplitude of the PC coefficient.…”

Section: Methodsmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

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Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.

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Section: Methodsmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

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“…Leaf spectral diversity reflects the variation trend of wheat leaf reflectance after different sampling methods (imaging or non-imaging) and interpolation reconstructions and reflects whether spectral information is lost after interpolation reconstruction [39]. Figure 4a,c reflects the variation trend of wheat leaf reflectance under different sampling methods.…”

Section: Preliminary Investigation By Pcamentioning

confidence: 99%

A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network

Ju,

Lv,

Weng

et al. 2023

Agronomy

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Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data.

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“…Korla fragrant pears belong to a pear species found in Xinjiang, China [1]. A thin pericarp, fine pulp, rich juice, and sweet and brittle taste make them popular among Chinese and international consumers [2,3]. Due to their thin pericarp and fine pulp, Korla fragrant pears have a high mechanical damage rate (15%) during harvest, transportation, grading, and packaging.…”

Section: Introductionmentioning

confidence: 99%

Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage

et al. 2023

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To increase the commercial value of damaged fragrant pears and improve marketing competitiveness, this study explored the degree of damage degree and effects of storage time on the internal quality of fragrant pears during storage and predicted the internal quality of fragrant pears using an adaptive neural fuzzy inference system (ANFIS). The internal quality prediction models of damaged fragrant pears during storage with eight membership functions were constructed, and the optimal model was chosen, allowing for accurate internal quality prediction of damaged fragrant pears. The research results demonstrated that the hardness and soluble solid content (SSC) of fragrant pears decrease as the storage time increases. Given the same storage time, the hardness and SSC of fragrant pears are negatively correlated to the degree of damage. The ANFIS modelling technique is feasible for predicting the internal quality of fragrant pears during storage. The best prediction performances for the hardness and SSC of fragrant pears, respectively, are displayed by the ANFIS using the input membership function of trimf (RMSE = 0.1362, R2 = 0.9752; RMSE = 0.0315, R2 = 0.9892). The findings of this study can be used to predict the storage quality of fruits.

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Inhibitory effect of CaCl₂ and carboxymethyl chitosan coating on the after‐ripening of Korla fragrant pears in cold storage

Cited by 10 publications

References 26 publications

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network

Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage

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Inhibitory effect of CaCl2 and carboxymethyl chitosan coating on the after‐ripening of Korla fragrant pears in cold storage

Cited by 10 publications

References 26 publications

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network

Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage

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Product

Resources

About

Inhibitory effect of CaCl₂ and carboxymethyl chitosan coating on the after‐ripening of Korla fragrant pears in cold storage