Classification of Apple Disease Based on Non-Linear Deep Features

Ayaz, Hamail; Rodrí­guez-Esparza, Erick; Oliva, Diego; Pérez‐Cisneros, Marco; Sarkar, Ram

doi:10.3390/app11146422

Cited by 30 publications

(10 citation statements)

References 55 publications

(60 reference statements)

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“…In the agro-industry fast and accurate fruit classification is the highest need. The fruits can be classified into different classes as per their external features like shape, size and color using some computer vision and deep learning techniques [4] , [5] , [6] , [7] , [8] . The FruitNet dataset was created to include Indian fruits along with its quality parameters for those which are highly consumed or exported as per [9] .…”

Section: Data Descriptionmentioning

confidence: 99%

FruitNet: Indian fruits image dataset with quality for machine learning applications

Meshram¹,

Patil²

2022

Data in Brief

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Fast and precise fruit classification or recognition as per quality parameter is the unmet need of agriculture business. This is an open research problem, which always attracts researchers. Machine learning and deep learning techniques have shown very promising results for the classification and object detection problems. Neat and clean dataset is the elementary requirement to build accurate and robust machine learning models for the real-time environment. With this objective we have created an image dataset of Indian fruits with quality parameter which are highly consumed or exported. Accordingly, we have considered six fruits namely apple, banana, guava, lime, orange, and pomegranate to create a dataset. The dataset is divided into three folders (1) Good quality fruits (2) Bad quality fruits, and (3) Mixed quality fruits each consists of six fruits subfolders. Total 19,500+ images in the processed format are available in the dataset. We strongly believe that the proposed dataset is very helpful for training, testing and validation of fruit classification or reorganization machine leaning model.

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Section: Data Descriptionmentioning

confidence: 99%

FruitNet: Indian fruits image dataset with quality for machine learning applications

Meshram¹,

Patil²

2022

Data in Brief

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“…The greatest requirement in the agro-industry is for quick and accurate vegetable classification. Utilizing computer vision and deep learning techniques, the veggies may be divided into many groups based on their outward characteristics, such as shape, size, and color [5] , [6] , [7] , [8] , [9] . Vegetables with quality parameters for those that are heavily consumed or exported in accordance with Agricultural & Processed Food Products Export Development Authority (APEDA) are included in this VegNet dataset [10] .…”

Section: Data Descriptionmentioning

confidence: 99%

VegNet: Dataset of vegetable quality images for machine learning applications

Suryawanshi¹,

Patil²,

Chumchu

2022

Data in Brief

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“…Using controlled rotation operations, the convolution layers minimize the feature map. By repeatedly following the above steps, the adequately connected layer operates on the qubit state in the same way as traditional convolutional neural network (CNN) models [74] does. Finally, the qubit state measurement is decoded into the system output of the required dimensions, and after each measurement, the circuit parameters are modified using a gradient descentbased optimizer as shown in figure 15.…”

Section: ) Quantum Neural Networkmentioning

confidence: 99%

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

2022

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Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning, become a quite challenging field in case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.

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Classification of Apple Disease Based on Non-Linear Deep Features

Cited by 30 publications

References 55 publications

FruitNet: Indian fruits image dataset with quality for machine learning applications

FruitNet: Indian fruits image dataset with quality for machine learning applications

VegNet: Dataset of vegetable quality images for machine learning applications

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

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