Fruit maturity estimation based on fuzzy classification

Hasan, Rija; Monir, Syed Muhammad G.

doi:10.1109/icsipa.2017.8120574

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…The characteristics extracted from each fruit's image were area, major and minor axis of each sample; these were used as inputs in the diffuse system for their classification. Another similar study was reported by [16], which…”

Section: Introductionsupporting

confidence: 84%

Fuzzy Classification of the Maturity of the Tomato Using a Vision System

et al. 2019

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Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.

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Section: Introductionsupporting

confidence: 84%

Fuzzy Classification of the Maturity of the Tomato Using a Vision System

et al. 2019

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“…We aim at developing a versatile visual inspection algorithm that is easy to configure and fast to perform grading tasks for our embedded smart camera. As discussed in the introduction, more sophisticated but powerful machine learning [12][13][14][15][16] and deep learning [17][18][19][20] approaches have been successfully applied to fruit and vegetable grading. Most of them are not suitable for embedded applications because of their computational complexity.…”

Section: Visual Inspection Algorithmmentioning

confidence: 99%

“…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). A fuzzy inference system was applied to fruit maturity classification using color features [12]. Artificial neural networks have been successfully used for sorting pomegranate fruits [13], apples [14], fruit grading based on external appearance and internal flavor [15], and color-based fruit classification [16].…”

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confidence: 99%

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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“…A comparison between the ripening index and the results of multivariate discriminant analysis of the colour parameters indicated that the maturity stages using colour can be classified with a rate of 100. An approach of guava maturity estimation was proposed by [18]. Heuristically, acquired hue and its corresponding saturation and lightness are the attributes of choice used to classify the sample into three classes; Raw, Ripe and Overripe.…”

Section: Related Workmentioning

confidence: 99%