A fruits recognition system based on a modern deep learning technique

Chung, Dang Thi Phuong; Tai, Dinh Van

doi:10.1088/1742-6596/1327/1/012050

Cited by 35 publications

(9 citation statements)

References 4 publications

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“…The authors of (18) and (3) used the same dataset fruits-360, but they could not achieve 100% validation and testing accuracy. Again, the same EfficientNet-b0 algorithm was used in (14) and (17) for the classification of fruits but could not achieve 100% validation accuracy and 99.90% testing accuracy as the proposed model has achieved. The classification accuracy of ImageNet dataset in (16) using CNN for 7 categories is 91.66%, while we achieved 91.28% for 11 categories with 100% testing accuracy.…”

Section: Discussionmentioning

confidence: 98%

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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Section: Discussionmentioning

confidence: 98%

Robust Convolutional Neural Network Model For Recognition of Fruits

Bongulwar¹,

Talbar²

2021

IJST

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“…Similarly, Khan et al [55] finds effect of cimate change on fruit by cointegration and machine learning methods with an accuracy of 90.00 ± 2%. Dang et al [56] presented a used convolution neural network (CNNs) and Efficient Net architecture for fruit recognition using the 360 fruit dataset. According to the results, the suggested model is 95% accurate.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

et al. 2021

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Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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“…These models can be used in health monitoring applications to observe fruit intake and calorie estimation. The data can be used by machine learning researchers/companies to develop models for recognizing different fruits [4] , [5] . The current research trends in deep learning and machine learning target mainly the development of applications for everyday use such as face recognition, fingerprint recognition, or application in the fields of healthcare, engineering, and many others.…”

Section: Value Of the Datamentioning

confidence: 99%

“…The data can be used by machine learning researchers/companies to develop models for recognizing different fruits [4] , [5] .…”

Section: Value Of the Datamentioning

confidence: 99%

“…The existing Fruits dataset (Fruits 360 [7] ) contains a smaller number of images and does not have variations and did not contain multiple fruit sets in the single images. In recent literature [1] , [2] , [3] , [4] , [5] , fruit recognition is mainly done using single fruit in an image, and does not contain multiple types of fruits in a single image. Therefore, there is a need to develop a comprehensive dataset containing a variety of fruits with different fruit set combinations captured on different plates’ sizes, shapes, and colors with varying angles, brightness levels, and distances.…”

Section: Objectivementioning

confidence: 99%

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DeepFruit: A dataset of fruit images for fruit classification and calories calculation

Latif,

Mohammad,

Alghazo

2023

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A fruits recognition system based on a modern deep learning technique

Cited by 35 publications

References 4 publications

Robust Convolutional Neural Network Model For Recognition of Fruits

Robust Convolutional Neural Network Model For Recognition of Fruits

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

DeepFruit: A dataset of fruit images for fruit classification and calories calculation

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