Fruits and vegetables quality evaluation using computer vision: A review

Bhargava, Anuja; Bansal, Atul

doi:10.1016/j.jksuci.2018.06.002

Cited by 343 publications

(212 citation statements)

References 97 publications

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“…All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture features (some include color and size) were designed for quality analysis.…”

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confidence: 99%

“…The installation is easier and space utilization is smaller than most computer-based vision systems. The operating cost is lower because of its ease of changeover for different products or different grading criteria.Besides the hardware, the most important element of the smart camera is a robust visual inspection algorithm to improve the efficiency of the quality evaluation process [10,11]. Unlike the general object recognition that includes a large number of classes, visual inspection usually has two (good or bad) or a very small number of classes, consistent lighting and a uniform image background.…”

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“…The biggest challenge for machine-learning based visual inspection algorithms is the collection of a large number of negative samples for training. A good visual inspection algorithm must be able to automatically construct needed distinctive features to achieve good performance [10,11].A review published in 2017 discussed methods for feature extraction for color, size, shape, and texture [11]. The authors also discussed machine learning methods for machine vision, including K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), and the latest developments in deep learning or convolutional neural network (CNN).…”

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confidence: 99%

“…A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture features (some include color and size) were designed for quality analysis.…”

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Smart Camera for Quality Inspection and Grading of Food Products

et al. 2020

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Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.Electronics 2020, 9, 505 2 of 18 or sorting machines for food products on the market have been designed to automate these tasks and have proven to be very successful.Many recent developments were aimed at broad grading applications. For example, a machine learning-based system was built to use color and texture features for tomato grading and surface defect detection [1]. Color and texture features were collected for maturity evaluation, defect detection, size and shape analysis for mangoes [2]. Guava fruits were classified into four maturity levels using the K-nearest neighbor (KNN) algorithm to analyze color distribution in HSI (Hue, Saturation, and Intensity) color space [3]. Image processing techniques were used for corn seed grading [4], plum fruit maturity evaluation [5], quality assessment of pomegranate fruits [6], and the ripeness of palm fruit [7]. Two grading systems for date maturity [8] and skin quality [9] evaluation were designed specifically for Medjool dates. All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture fe...

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Smart Camera for Quality Inspection and Grading of Food Products

et al. 2020

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Image Processing Technology, Imaging Techniques, and Their Application in the Food Processing Sector

Ghanghas,

Kumar,

Singh

et al. 2024

Nonthermal Food Engineering Operations

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Varietal quality control in the nursery plant industry using computer vision and deep learning techniques

Borraz-Martínez

Tarres

Boqué

et al. 2020

Journal of Chemometrics

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Computer vision coupled to deep learning is a promising technique with multiple applications in the industry. In this work, the potential of this technique has been assessed in the classification of two varieties of almond trees (Prunus dulcis), Soleta and Pentacebas. For that, a convolutional neural network named VGG16 was used. The most appropriate configuration for model training was studied, which included the comparison between two different filling modes (reflect and nearest) in the data augmentation step, the evaluation of the batch size and the analysis of the image sizes. The robustness of the model was also checked, and information was obtained about how the model extracts the information from the images. The results showed that the reflect fill mode was more effective than the nearest one. The best results were obtained using batches with 30 and 40 images, with an image size of (224 × 224) pixels. The verification of the robustness proved the capability of the technique as a promising tool for plant varietal identification.

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Fruits and vegetables quality evaluation using computer vision: A review

Cited by 343 publications

References 97 publications

Smart Camera for Quality Inspection and Grading of Food Products

Smart Camera for Quality Inspection and Grading of Food Products

Image Processing Technology, Imaging Techniques, and Their Application in the Food Processing Sector

Varietal quality control in the nursery plant industry using computer vision and deep learning techniques

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