Automatic Fruit Classification Using Deep Learning for Industrial Applications

Hossain, M. Shamim; Al-Hammadi, Muneer; Muhammad, Ghulam

doi:10.1109/tii.2018.2875149

Cited by 259 publications

(126 citation statements)

References 30 publications

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“…In the papers [15], [17], and [18] performed fruit recognition but accuracy and classifier are not mentioned. Paper [19] applied deep learning for fruit recognition and they have achieved good accuracy.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…Several classifiers models and features are discussed in their work but they did not propose a computer vision approach for classifying or detecting fruits or fruits diseases and so on. Hossain et al [19] classify fruits using deep learning technique for industrial applications. The proposed model is based on convolutional neural network and pre-trained VGG-16 (also called OxfordNet) deep learning model.…”

Section: Literature Reviewmentioning

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%

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Local Fruit Recognition

Mia¹,

Mia²,

Majumder³

et al. 2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Visual perception is to interpret the environment by the light (in the form of images captured by various cameras) reflected by the objects via image analysis [1] and is now finding a wide range of applications in smart society (e.g. transportation surveillance [2], aircraft detection [3], smart health [4], industrial inspection [5]). Following this line of thought, this work aims to exploit aerial visual perception in smart farming to tackle the grand challenge facing modern agriculture: feeding a growing world population with an ageing structure while protecting the environment.…”

Section: Introductionmentioning

confidence: 99%

Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring

et al. 2021

IEEE Trans. Ind. Inf.

117

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Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This work exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms including UAV sensing, multispectral imaging, vegetation segmentation and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labelled for system evaluation by inspecting highresolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested including three RGB bands and five selected spectral vegetation indices by Sequential Forward Selection strategy of Wrapper algorithm.

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“…By contrast with traditional machine learning solutions, deep learning techniques are undergoing rapid development. Applications of deep learning involve information retrieval [4], natural language processing [5], human voice recognition [6], computer vision [7], anomaly detection [8], recommendation systems [9], bioinformatics [10], medicine [11,12], crop science [13], earth science [14], robotics [15][16][17][18], transportation engineering [19], communication technologies [20][21][22], and system simulation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Chen

Zhang

et al. 2020

Mobile Information Systems

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Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.

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Automatic Fruit Classification Using Deep Learning for Industrial Applications

Cited by 259 publications

References 30 publications

Computer Vision Based Local Fruit Recognition

Computer Vision Based Local Fruit Recognition

Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring

Deep Learning on Computational-Resource-Limited Platforms: A Survey

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