Automatic Classification and Detection of Oranges Based on Computer Vision

Chen, Yuwei; Wu, Jinghua; Cui, Mengtian

doi:10.1109/compcomm.2018.8780680

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Surface injuries include fruit blemishes, burns, and wrong structures. [18,30,32,38,40,43,49,55,61,71,77,79,82,83,85,87] Chilling/freezing disorders This post-harvest disorder occurs due to the chilling effect, and it is a catastrophic disorder that evolves with the storage of fruits at low temperatures that notably demotes the quality of citrus fruits in the market or disqualifies them from the market. The impact of the chilling disorder depends upon the temperature at which fruits are to be stored or the duration of the time spent by the fruit in cold storage.…”

Section: Wind Scarmentioning

confidence: 99%

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Dhiman¹,

Kaur

Balasaraswathi

et al. 2023

Sustainability

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Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.

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Section: Wind Scarmentioning

confidence: 99%

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Dhiman¹,

Kaur

Balasaraswathi

et al. 2023

Sustainability

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“…They tested the proposed model using 50 Chokun orange samples, obtaining a 98% accuracy. Chen et al [22] proposed an orange sorting detection by obtaining four main features of the oranges, including fruit surface color, size, surface defect, and shape using image processing. They trained a BackPropagation neural network with these features.…”

Section: Related Workmentioning

confidence: 99%

“…We proposed an FPGA due to the performance and power consumption advantages over conventional microcontrollers or even multi-core processors or GPU devices. • Most of the related works use complex learning techniques, such as SVMs [22,24], Quadtrees [8], KNNs [56], and CNNs [43]. The Decision Rules are the most similar approach to ours, proposed in [21].…”

Section: Comparison Of the Proposed System To Related Workmentioning

confidence: 99%

Real-Time Embedded Vision System for Online Monitoring and Sorting of Citrus Fruits

Nuño-Maganda,

Dávila-Rodríguez,

Hernández-Mier

et al. 2023

Electronics

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Citrus fruits are the second most important crop worldwide. One of the most important tasks is sorting, which involves manually separating the fruit based on its degree of maturity, and in many cases, involves a task carried out manually by human operators. A machine vision-based citrus sorting system can replace labor work for the inspection of fruit sorting. This article proposes a vision system for citrus fruit sorting implemented on a dedicated and efficient Field Programmable Gate Array (FPGA) hardware architecture coupled with a mechanical sorting machine, where the FPGA performs fruit segmentation and color and size classification. We trained a decision tree (DT) using a balanced dataset of reference images to perform pixel classification. We evaluate the segmentation task using a pixel accuracy metric, defined as the ratio between correctly segmented pixels produced by a DT and the total pixels in the reference image segmented offline using Otsu’s thresholding algorithm. The balance between correctly classified images by color or size and their corresponding labels of that color and size evaluates the color and size classification algorithms. Considering these metrics, the system reaches an accuracy of 97% for fruit segmentation, 94% for color classification, and 90% for size classification, running at 60 fps.

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“…A system for classifying diseases of orange using multiclass Support Vector Machines (SVM) and calculating the severity of diseases using fuzzy logic is proposed in ( Behera et al, 2018 ). The automatic citrus grading detection is performed by using BP neural network ( Chen et al, 2018 ). These methods are interpretable and have the features of a high correct recognition rate compared with the manual method but the tediousness of the feature extraction process and the loss of features due to dimensionality reduction are challenges that need to be addressed ( Chao et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

An efficient convolutional neural network-based diagnosis system for citrus fruit diseases

Huang,

Jiang,

Huang

et al. 2023

Front. Genet.

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Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance.Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network.Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease.Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.

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Automatic Classification and Detection of Oranges Based on Computer Vision

Cited by 9 publications

References 2 publications

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Real-Time Embedded Vision System for Online Monitoring and Sorting of Citrus Fruits

An efficient convolutional neural network-based diagnosis system for citrus fruit diseases

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