Deep Learning Based on Residual Networks for Automatic Sorting of Bananas

Helwan, Abdulkader; Ma’aitah, Mohammad Khaleel Sallam; Abiyev, Rahib H.; Uzelaltinbulat, Selin; Sonyel, Bengi

doi:10.1155/2021/5516368

Cited by 18 publications

(13 citation statements)

References 31 publications

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“…Machine learning has been a crucial "catalyst" in the 60 years of development. Machine learning is to make the computer learn repeatedly to improve its performance and intelligently identify new samples through relevant algorithms, so that the computer can make correct responses and behaviors without explicit programming control [4]. In the past decade, machine learning has been widely applied in various fields such as autonomous driving, speech recognition, data mining, medical diagnosis, weather forecast, and industrial control.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization Design of Automatic Sorting Robot Process

Liu

Wang

2022

Journal of Sensors

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In order to further improve the efficiency of sorting robots, an algorithm based on artificial intelligence design is evaluated. On the basis of determining the design requirements of belt conveyor and motor selection, the reverse device and return device and camera bracket sorting car are redesigned, and the sorting car is further analyzed by finite element method. All programs are written based on PLC. The results show that the recognition and sorting rate of 100 express surface orders with the same specifications reaches 100% and the sorting rate reaches 94% by analyzing the data recorded in the database, and the average time consumed in each recognition process is 0.0493097 s and 0.047133 s, respectively, in basic and simulated recognition. The difference did not exceed 0.01 s, and the time was basically the same. Therefore, the scheme verifies the rationality of mechanical structure design, visual scheme design, and control system design.

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Section: Introductionmentioning

confidence: 99%

Intelligent Optimization Design of Automatic Sorting Robot Process

Liu

Wang

2022

Journal of Sensors

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“…In (7) , the researchers used a 9-layer convolutional neural network with data augmentation for the classification of fruits. It showed that with data augmentation they obtained an average precision of 96.34%.The architecture of a deep learning structure for rating bananas as healthy or defective is shown in the study (8) . The "ResNet-50" residual learning-based network was created to sort bananas.…”

Section: Introductionmentioning

confidence: 92%

Robust Convolutional Neural Network Model For Recognition of Fruits

Bongulwar¹,

Talbar²

2021

IJST

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Objectives: To develop a model for the automatic recognition of fruits utilizing deep learning techniques. Methods: We have designed a fruit classification and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 different categories. The classification is challenging as the images comprise different fruits of the same color and shape, overlapped fruits, the background is not homogenous, and with different light effects etc. Findings: We have achieved a validation accuracy of 91.28 % and the testing accuracy of 100%. The same model is trained on the fruits-360 dataset with 92 categories of fruits with 47,526 images. The Model gives validation accuracy of 100% and testing accuracy of 100%. This study also compares the results obtained using transfer learning by training the EfficientNet-b0 architecture with the ImageNet and fruits-360 dataset. The validation accuracy is 96.77% and the testing accuracy is 100%. Again, the validation accuracy and testing accuracy for fruits-360 dataset is 100% and 99.9% respectively. Applications: Recognition of fruit is required in agricultural problems like robot harvesting and fruit counting and many more applications. Moreover, it can be used in the retail business, as a self-service system to recognize the fruits. It can be used in human-robot interactions. Novelty: The model once trained can achieve state-of-art accuracy for the recognition of any type of fruit with any background. It sometimes exceeds human-level performance. Hence, the Model is Robust enough to recognize the fruits.

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“…Pu et al proposed a method for identifying the origins of tangerine peel using terahertz timedomain spectroscopy combined with CNN (convolutional neural network) [14]. Diferent spectral data were used to Deep learning [15] is a machine learning technique in which machines simulate the human brain to analyze data, with the computer vision [16] being one of the more prominent applications. In the past few years, the image classifcation of agricultural products represented by tangerine peel is emerging.…”

Section: Related Workmentioning

confidence: 99%

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Deng,

Li,

et al. 2024

Journal of Food Quality

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Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.

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Deep Learning Based on Residual Networks for Automatic Sorting of Bananas

Cited by 18 publications

References 31 publications

Intelligent Optimization Design of Automatic Sorting Robot Process

Intelligent Optimization Design of Automatic Sorting Robot Process

Robust Convolutional Neural Network Model For Recognition of Fruits

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

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