scholarly journals Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters

2020 ◽  
Vol 12 (19) ◽  
pp. 3258
Author(s):  
Jiangsan Zhao ◽  
Dmitry Kechasov ◽  
Boris Rewald ◽  
Gernot Bodner ◽  
Michel Verheul ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Hyperspectral Image ◽  
Absolute Error ◽  
Solid Content ◽  
Image Resolution ◽  
Soluble Solid Content ◽  
Quality Properties ◽  
Tomato Quality ◽  
Rgb Images

Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.

scholarly journals Effect of Cross-Linked Alginate/Oil Nanoemulsion Coating on Cracking and Quality Parameters of Sweet Cherries

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 449
Author(s):  
Camilo Gutiérrez-Jara ◽  
Cristina Bilbao-Sainz ◽  
Tara McHugh ◽  
Bor-Sen Chiou ◽  
Tina Williams ◽  
...  
Keyword(s):  
Titratable Acidity ◽  
Quality Parameters ◽  
Solid Content ◽  
Postharvest Quality ◽  
Soluble Solid Content ◽  
Nutritional Values ◽  
Quality Properties ◽  
Vapor Transfer ◽  
Sweet Cherries ◽  
Soluble Solid

The cracking of sweet cherries causes significant crop losses. Sweet cherries (cv. Bing) were coated by electro-spraying with an edible nanoemulsion (NE) of alginate and soybean oil with or without a CaCl2 cross-linker to reduce cracking. Coated sweet cherries were stored at 4 °C for 28 d. The barrier and fruit quality properties and nutritional values of the coated cherries were evaluated and compared with those of uncoated sweet cherries. Sweet cherries coated with NE + CaCl2 increased cracking tolerance by 53% and increased firmness. However, coated sweet cherries exhibited a 10% increase in water loss after 28 d due to decreased resistance to water vapor transfer. Coated sweet cherries showed a higher soluble solid content, titratable acidity, antioxidant capacity, and total soluble phenolic content compared with uncoated sweet cherries. Therefore, the use of the NE + CaCl2 coating on sweet cherries can help reduce cracking and maintain their postharvest quality.

scholarly journals Application of Thermosonication in Red Pitaya Juice Processing: Impacts on Native Microbiota and Quality Properties during Storage

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1041
Author(s):  
Wenxian Zhu ◽  
Yana Ai ◽  
Fang Fang ◽  
Hongmei Liao
Keyword(s):  
Solid Content ◽  
Soluble Solid Content ◽  
Thermal Methods ◽  
Promising Alternative ◽  
Quality Properties ◽  
Juice Processing ◽  
Color Retention ◽  
Quality Retention ◽  
Soluble Solid ◽  
Yeasts And Molds

The effects of thermosonication (TS) on microbial safety and quality of red pitaya juice during storage were assessed in this study. Freshly prepared red pitaya juices were thermosonicated at 475 W and 56 °C for 20 min. Upon TS processing, native microbiota including aerobic bacteria, yeasts, and molds reduced to less than 10 CFU/mL. Their growth during storage were slow and equal to thermal-processed (83 °C, 1.5 min) samples. During storage at 4 °C for 28 days, soluble solid content, pH, activities of polyphenol oxidase and peroxidase, and browning degree remained unchanged. A visible color decay was observed in TS-processed samples at day 10, mainly resulting from decomposition of betacyanins and the growth of residual native microbiota. Compared to thermal-treated juices, better color retention was obtained by TS treatment. Therefore, TS is a promising alternative technology of thermal methods of juice processing, with equal shelf life and better quality retention effects.

scholarly journals Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5021
Author(s):  
Baohua Yang ◽  
Yuan Gao ◽  
Qian Yan ◽  
Lin Qi ◽  
Yue Zhu ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Solid Content ◽  
Internal Quality ◽  
Accurate Estimation ◽  
Soluble Solid Content ◽  
Model Based ◽  
Soluble Solid ◽  
Non Destructive

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

Role of Phospholipase D Inhibitor in Regulating Expression of Senescencerelated Phospholipase D gene in Postharvest Longan Fruit

2019 ◽  
Vol 14 (7) ◽  
pp. 649-657 ◽  
Author(s):  
Li Li ◽  
Jiemin Li ◽  
Jian Sun ◽  
Ping Yi ◽  
Changbao Li ◽  
...  
Keyword(s):  
Phospholipase D ◽  
Ascorbic Acid Content ◽  
Titratable Acidity ◽  
Significant Inhibition ◽  
Solid Content ◽  
Total Soluble Solid ◽  
Total Phenolic ◽  
Soluble Solid Content ◽  
Soluble Solid ◽  
Pericarp Browning

Background: Phospholipase D (PLD)is closely related to browning and senescence of postharvest longan fruit. Objective: This study investigated the effects of 2-butanol (a PLD inhibitor) on the expression and regulation of PLD during storage of longan fruit at a low temperature. Methods: Senescence-related quality indices showed that the 2-butanol-treated fruit presented lower pericarp browning index, pulp breakdown index and total soluble solid value than the untreated fruit. Results: The fruit treated by 60 µL/L 2-butanol exhibited the strongest inhibition on senescence, which significantly delayed changes in weight, titratable acidity content, total soluble solid content and ascorbic acid content. This treatment maintained a high level of total phenolic content and caused significant inhibition on pericarp browning and pulp breakdown. Through ELISA method, 60 µL/L 2-butanol treatment also reduced PLD activity. Real-time RT-PCR (RT-qPCR) results showed that PLD mRNA expression level was inhibited by 60 µL/L 2-butanol within 15 days. Western-blotting results further confirmed the differential expression of PLD during storage, and a relatively higher expression for PLD protein was found in control compared to the 2-butanoltreated fruit during 15-d storage. Conclusion: These results provided a scientific basis and reference to further investigating postharvest longan quality maintenance by regulating the PLD gene expression.

scholarly journals Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming Fang ◽  
Yoann Altmann ◽  
Daniele Della Latta ◽  
Massimiliano Salvatori ◽  
Angela Di Fulvio
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Image Resolution ◽  
Emission Tomography ◽  
Nuclear Safeguards ◽  
Activity Levels ◽  
Spent Fuel ◽  
Gamma Emission ◽  
Cross Sectional ◽  
Fuel Rods

AbstractCompliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

scholarly journals Hyperspectral Image Reconstruction via Block Low-Rank and Three-Dimension Weighted Total Variation Constraint

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47698-47713 ◽  
Author(s):  
Zongrui Wu ◽  
Xi Chen ◽  
Wenxuan Shi ◽  
Liqiong Chen ◽  
Shiyong Hu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Three Dimension ◽  
Weighted Total Variation

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

Investigation of Heat Transfer for Shell-Tube Fuel-Cooled Heat Exchanger

2013 ◽  
Vol 378 ◽  
pp. 459-465
Author(s):  
Ya Guo Lu ◽  
Peng Fei Zhu
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Relative Error ◽  
Absolute Error ◽  
Heat Transfer Characteristics ◽  
Shell Side ◽  
Transfer Characteristics ◽  
Aero Engine ◽  
The Absolute

A calculate method based on ε-NTU model for heat transfer characteristics of shell-tube fuel-cooled heat exchanger of aero-engine lubrication system was built. The heat convection coefficient was obtained by a dimensionless curve (Re~StPr2/3), which was detailed introduced as well. A case study was executed at last. The absolute error of the outlet lubrication of the tube side and the shell side between the value of calculation and experiment was less than ±10°C, and the relative error was less than 6.5%. The absolute error of the heat transferred between calculation and experiment was less than ±0.9kW, and the relative error was less than 7.4%. It indicates that the mothod is available for the investigation of heat transfer characteristics of shell-tube fuel-cooled heat exchanger.

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