Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing

Firouz, Mahmoud Soltani; Sardari, Hamed

doi:10.1007/s12393-022-09307-1

Cited by 33 publications

(11 citation statements)

References 110 publications

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“…Edge-based segmentation techniques are based on marking discontinuities of numerous factors such as colors, gray levels, and others [103]. These techniques blend detected edges into edge chains for constructing borders or object boundaries.…”

Section: Edge-based Segmentationmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: Edge-based Segmentationmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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“…Digital image processing technology emerged, evolved, and matured alongside the advancements in computer technology and VLSI (Very Large Scale Integration) during the 1960s [9]. Soltani Firouz and Sardari suggested combining machine vision and image processing to assist in detecting defective product areas and classifying products based on quality and defect types [10]. Tao, Huawei, and colleagues have introduced a novel texture feature extraction algorithm called the color complete local binary pattern (CCLBP).…”

Section: Vegetable Recognition System Based On Traditional Mechanical...mentioning

confidence: 99%

A simple and efficient deep learning-based framework for vegetable recognition

Gong

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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Since the 21st century, artificial intelligence has been continuously evolving in various fields, particularly in agriculture. Vegetables, as a critical component of agriculture and human diets, have always been a focal point in terms of cultivation, production, and sales. Compared to traditional vegetable classification that requires professional knowledge and experience, AI technology utilizes computer vision to achieve automated classification. This study presents a deep learning-based vegetable recognition system aimed at automating the identification and classification of vegetables. The system utilizes a convolutional neural network (CNN) as its fundamental algorithm, integrating the traditional CNN architecture, which comprises convolutional layers, pooling layers, and fully connected layers. In comparison to other vegetable recognition systems on the market, this system utilizes a simpler architecture for processing and classifying vegetable images, significantly improving the accuracy and compatibility of identification. The research steps comprise data collection, image preprocessing, model training, and model testing. Experimental results demonstrate that the system can rapidly and accurately identify and classify various vegetables, with an average accuracy rate exceeding 95% on the test dataset, showcasing high practical value.

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“…In recent years, with the rapid development of intelligent manufacturing, machine vision technology has been employed in product integrity detection, 1 dimension measurement, 2 , 3 and defect detection 4 – 6 When detecting multi-surface with regular shapes, parallel imaging is typically employed to conduct detection tasks on each surface. As only one surface can be detected at a time, the part either needs to be flipped or multiple cameras must be used for synchronized detection, this frequently results in the development of complex mechanical structures, thereby diminishing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Ye,

Luo,

Zeng

et al. 2024

J. Electron. Imag.

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Feature detection on multi-surface textureless metal parts is a common and crucial task in smart manufacturing. Traditional visual detection methods typically detect only one surface at a time, leading to inefficiency. In this work, we propose a framework for the rapid detection of multi-surface features on metal parts based on a non-parallel imaging method. The framework is built upon the principles of wireframe modeling and feature modeling, combining a lightweight neural network with a traditional template matching method. First, the line segment profile features of metal parts are extracted based on a lightweight neural network. The part is then represented by the correlation descriptors of the line segments. The initial bitmap of the part is obtained through template matching, followed by obtaining the accurate six-dimensional (6D) bitmap of the metal part through iterative matching of endpoints. Subsequently, utilizing the 6D pose information, the visible surface of the part undergoes transformation through orthographic projection, and the conformity of the transformed surface is assessed. Finally, experimental validation was conducted using a hydraulic valve as the object. The experimental results indicated that our method accurately estimates the bit-pose of textureless images. Moreover, it can concurrently perform visual detection tasks on multiple visible surfaces by employing a straightforward orthographic projection transformation.

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Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing

Cited by 33 publications

References 110 publications

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A simple and efficient deep learning-based framework for vegetable recognition

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

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