Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System

Hussain, Ibrar; He, Qian; Chen, Zhuliang

doi:10.5121/ijcsa.2018.8301

Cited by 35 publications

(23 citation statements)

References 16 publications

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“…In the work, it is reported that in the context of fruit recognition system SVM classifier works far better than other classifiers. The objective of the research [6] is to recognize the fruit using probability mechanism algorithm. Although they achieved good accuracy, the segmentation technique and feature set are not mentioned.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

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Computer Vision Based Local Fruit Recognition

Mia¹,

Mia²,

Majumder³

et al. 2019

IJEAT

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Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research.

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Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…Hussain et al [6] proposed an algorithm based on deep convolution neural network for fruit recognition where their contribution is to build a database of fruit image of only 15 categories which is very few compared with the huge categories of fruits and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Local Fruit Recognition

Mia¹,

Mia²,

Majumder³

et al. 2019

IJEAT

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“…Deep learning frameworks achieved excellent performance in many fields [75], [80]. Researchers in image steganography and steganalysis also demonstrated to explore the capability of deep learning algorithms in various key areas of multimedia security.…”

Section: Image Steganalysis Based On Deep Learningmentioning

confidence: 99%

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

2020

KSII TIIS

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Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.

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“…Hussain, I., He, Q. and Chen, Z. [8] developed a system using deep convolutional neural network model to extract fruit image features and implement classification. Sriram, R., M, A. T. and Girija, P. J.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Optimum Fruit Maturity Grading

Gururaj*,

Vinod

2019

IJITEE

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Metabolic changes to the climacteric fruits like Mango during the post-harvest lifecycle has significant impact on the marketability of the fruit. One such change is in the structure of the cuticle layer on the surface of the mango and its composition changes as the fruit matures and expands. By looking at the structure of the cuticle the right maturity stage of the fruit can be determined which can decrease storage costs and increase market outcomes. The objective of this paper is to classify the mango fruit according to its maturity stage by looking at the cuticle structure of the mango fruit images as seen using light microscope under magnification. The data set consists of structural microscopic images of 2 varieties of mango viz. Banganapalli and Kili Mooku/Banglora and the classification is done using Convolutional Neural network (CNN) based on deep learning techniques. The combined results of both varieties yield a classification accuracy of 83% for all maturity stages. With this neural network model one can identify the ripening stage of the mango in a non-destructive manner that can greatly improve the mango harvesting strategies

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Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System

Abstract: ABSTRACT

Cited by 35 publications

References 16 publications

Computer Vision Based Local Fruit Recognition

Computer Vision Based Local Fruit Recognition

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

Predictive Model for Optimum Fruit Maturity Grading

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