Assessment of Strawberry Ripeness Using Hyperspectral Imaging

Shao, Yuanyuan; Wang, Yongxian; Xuan, Guantao; Gao, Zongmei; Hu, Zhichao; Gao, Chong; Wang, Kaili

doi:10.1080/00032719.2020.1812622

Cited by 31 publications

(21 citation statements)

References 41 publications

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“…The vibrational attributes of C–H, H–O, C–O, and N–H bonds in the food system can be easily studied by employing the HSI system [ 140 ]. Compared to traditional computer vision and human vision, the HSI system has natural advantages that can highlight some of the problematic or impossible features to extract with conventional computer vision systems [ 141 , 142 ]. With the advancement in optical sensing and imaging approaches, the HSI system has recently become a scientific and effective tool for monitoring and evaluating the quality of fruits and vegetables.…”

Section: Non-destructive Techniques and Food Quality Evaluationmentioning

confidence: 99%

Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview

Manzoor

Hussain

Tazeddinova

et al. 2022

Computational Intelligence and Neuroscience

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Among different fruits, mulberry is the most highlighted natural gift in its superior nutritional and bioactive composition, indispensable for continuing a healthy life. It also acts as a hepatoprotective immunostimulator and improves vision, anti-microbial, anti-cancer agent, anti-stress activity, atherosclerosis, neuroprotective functions, and anti-obesity action. The mulberry fruits also help reduce neurological disorders and mental illness. The main reason for that is the therapeutic potentials present in the nutritional components of the mulberry fruit. The available methods for assessing mulberry fruits are mainly chromatographic based, which are destructive and possess many limitations. However, recently some non-invasive techniques, including chlorophyll fluorescence, image processing, and hyperspectral imaging, were employed to detect various mulberry fruit attributes. The present review attempts to collect and explore available information regarding the nutritional and medicinal importance of mulberry fruit. Besides, non-destructive methods established for the fruit are also elaborated. This work helps encourage many more research works to dug out more hidden information about the essential nutrition of mulberry that can be helpful to resolve many mental-illness-related issues.

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Section: Non-destructive Techniques and Food Quality Evaluationmentioning

confidence: 99%

Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview

Manzoor

Hussain

Tazeddinova

et al. 2022

Computational Intelligence and Neuroscience

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“…As for fruit maturity evaluation by hyperspectral imaging, spectral features (Zhang et al, 2016;Shao et al, 2020), image features (Elmasry et al, 2007;Zhang et al, 2016;Gao et al, 2020), and the fusion of these features were used as inputs of classification models (Zhang et al, 2016;Khodabakhshian and Emadi, 2017). The spectral features were the most widely used.…”

Section: Discussionmentioning

confidence: 99%

“…Liu et al (2019) used hyperspectral imaging to detect the decay of postharvest strawberry. Shao et al (2020) used hyperspectral imaging to evaluate the strawberry ripeness. Weng et al (2020) used hyperspectral imaging to determine the soluble solid content (SSC), pH, and vitamin C in strawberry.…”

Section: Introductionmentioning

confidence: 99%

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Zhang

Yan

et al. 2021

Front. Plant Sci.

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Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

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“…The authors used a partial least square algorithm, and 90% accuracy was achieved. Shao et al (2020) [25] conducted a lab-based study to determine strawberry ripeness using 400-1000 nm hyperspectral data. A machine learning classification model was used, and 100% accuracy was achieved.…”

Section: Introductionmentioning

confidence: 99%

“…As this study was lab-based, a uniform light source was used with a constant sensor view angle, resulting in high classification accuracy. In another study, Gao et al (2020) [26] collected on-farm 400-1000 nm hyperspectral data to estimate strawberry ripeness. The authors used a deep learning approach on the 400-1000 nm hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

2022

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We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

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Assessment of Strawberry Ripeness Using Hyperspectral Imaging

Cited by 31 publications

References 41 publications

Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview

Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

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