Application of neural networks in predicting the qualitative characteristics of fruits

Abdelbasset, Walid Kamal; Nambi, Gopal; Elkholi, Safaa M.; Eid, Mahmoud M. A.; Alrawaili, Saud M; Mahmoud, Mustafa Z.

doi:10.1590/fst.118821

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Its powerful self-learning ability can automatically extract and learn the feature information in the image without additional supervision and training. In recent years, it has been gradually applied to defect detection, image recognition and other fields (Abdelbasset et al, 2022;Zhu et al, 2022). In traditional machine learning, the sample features are mainly obtained by means of feature transformation.…”

Section: Discussionmentioning

confidence: 99%

Jujube defect recognition method based on boosted convolutional neural network

2023

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In order to solve the problem of difficult and slow identification of jujube defects, a convolutional neural network model based on boosted EfficientNetv2 was proposed by taking the dry, cracked, broken and normal jujube in jujube as the research object. First, optimize the model structure, and set of the first Fused_MBConv in EfficientNetv2 3*3 convolution improved to parallel 1*1 convolution, 3*3 convolution and two serial 3*3 convolution. With reference to the CSPNet idea, one part of the convolved feature map in the MBConv module is directly spliced across channels, the other part is output through the dense block through the transition layer, and then spliced with the feature map in the first part; Then, the original Swish activation function is replaced by the optimal FReLU activation function; Finally, the Coordinate Attention module is introduced to embed the position information into the channel attention to optimize the model. The experimental results showed that the recognition rates of dry jujube, cracked jujube, broken jujube and normal jujube were 95.32%, 98.79%, 98.19% and 97.81% respectively, and the average recognition rate was 97.39%. Compared with other algorithms, the model has faster speed and higher recognition accuracy for defective jujube.

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Section: Discussionmentioning

confidence: 99%

Jujube defect recognition method based on boosted convolutional neural network

2023

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“…Thus, it is necessary to establish a non-destructive evaluation technique for predicting fruit quality. The pomegranate fruit is the most significant fruit in subtropical and tropical regions like India, Afghanistan, Mediterranean countries, Iran and the Middle East [1]. The pomegranate fruit has an increasing demand because of its high nutrition and multi-functionality.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Non-Destructive Evaluation of Physiochemical Parameters for Post Harvested Pomegranate Fruits

Yakatpure¹,

Rasane²,

Babu³

2022

INDJCSE

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In recent years, there has been an increasing consumer demand for fresh fruits of improved quality. Nowadays, pomegranate fruits attained increased popularity because of their high nutritional values and pharmacological characteristics. In order to assess the fruit's quality, the prediction of physiochemical parameters is necessary. Thus, the proposed work utilized a deep learning model for prediction analysis. The proposed work has studied a non-destructive determination of physiochemical parameters for pomegranate fruits. The internal images of the pomegranate fruit are obtained using magnetic resonance imaging (MRT). Then, the fruit quality is determined by predicting the physiochemical parameters like TSS, pH, acidity and firmness by combining the features of physiochemical and the GLCM (Gray Level Co-occurrence Matrix). The proposed study used the LSTM (Long-Short Term Memory) model and Stacked Dense Deep LSTM (SDD-LSTM) for prediction purposes. The performance of both the LSTM and SDD-LSTM based prediction models is measured by evaluating the metrics like a square of the correlation coefficient (r 2 ), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). Also, the obtained results of the proposed prediction models, along with the combined features, are compared with other machine learning techniques. The result analysis shows that the proposed SDD-LSTM and GLCM have a higher potential for non-destructive assessment of physicochemical values of pomegranate fruit, which may be helpful in fruit post-harvest management.

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Quantitative damage detection of direct maize kernel harvest based on image processing and BP neural network

Zhu

Han

et al. 2022

Food Sci. Technol

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Application of neural networks in predicting the qualitative characteristics of fruits

Cited by 3 publications

References 23 publications

Jujube defect recognition method based on boosted convolutional neural network

Jujube defect recognition method based on boosted convolutional neural network

Deep Learning based Non-Destructive Evaluation of Physiochemical Parameters for Post Harvested Pomegranate Fruits

Quantitative damage detection of direct maize kernel harvest based on image processing and BP neural network

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