Particle Swarm Optimized Fuzzy Model for the Classification of Banana Ripeness

Marimuthu, Senthilarasi; Roomi, S. Mohamed Mansoor

doi:10.1109/jsen.2017.2715222

Cited by 47 publications

(29 citation statements)

References 25 publications

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“…Tan et al [22] proposed a stepwise, computer vision-based algorithm to recognize the maturity stages of blueberries (mature, intermediate and young), and the recognition pipeline attained an average accuracy of 92.07%; specifically, the fruit regions were first located using histogram-oriented gradients and feature attributes in the International Commission on Illumination (CIE) L*a*b* color space, and then the maturity of a located blueberry was determined using template matching with a weighted Euclidean distance. Marimuthu et al [23] formulated a particle swarm optimized fuzzy model to grade banana fruits into unripe, ripe and overripe stages using peel color attributes extracted from the hue channel and opponent colors in CIE L*a*b* space, achieving an average classification accuracy of 93.11%.…”

Section: Introductionmentioning

confidence: 99%

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques

Zhuang

Hou

Tang

et al. 2019

Sensors

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The maturity stage of bananas has a considerable influence on the fruit postharvest quality and the shelf life. In this study, an optical imaging based method was formulated to assess the importance of different external properties on the identification of four successive banana maturity stages. External optical properties, including the peel color and the local textural and local shape information, were extracted from the stalk, middle and tip of the bananas. Specifically, the peel color attributes were calculated from individual channels in the hue-saturation-value (HSV), the International Commission on Illumination (CIE) L*a*b* and the CIE L*ch color spaces; the textural information was encoded using a local binary pattern with uniform patterns (UP-LBP); and the local shape features were described by histogram of oriented gradients (HOG). Three classifiers based on the naïve Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were adopted to evaluate the performance of identifying banana fruit maturity stages using the different optical appearance features. The experimental results demonstrate that overall identification accuracies of 99.2%, 100% and 99.2% were achieved using color appearance features with the NB, LDA and SVM classifiers, respectively; overall accuracies of 92.6%, 86.8% and 93.4% were obtained using local textural features for the three classifiers, respectively; and overall accuracies of only 84.3%, 83.5% and 82.6% were obtained using local shape features with the three classifiers, respectively. Compared to the complicated calculation of both the local textural and local shape properties, the simplicity and high accuracy of the peel color property make it more appropriate for identifying banana fruit maturity stages using optical imaging techniques.

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Section: Introductionmentioning

confidence: 99%

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques

Zhuang

Hou

Tang

et al. 2019

Sensors

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“…In addition, quantitative analysis [60,61] is performed using the spatial information obtained. The color features extracted are also used for maturity evaluation [57,62,63] and nutrient content detection [64,65]. The thermal data, meanwhile, proves to be useful in similar occasions of bruise detection [66,67], disease detection [68,69] and maturity evaluation [70,71] by analyzing the temperature variations over the inspected sample.…”

Section: Optics and Photonics Applications In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

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“…Color spaces HSV and CIE were used by Marimuthu and Roomi [15] to formulate fuzzy model for banana classification in unripe, ripe, and overripe stages. Hue channel (H) in HSV color was used because it represents the true color of banana peels.…”

Section: Stagementioning

confidence: 99%

“…Other techniques are not considered for comparison because they depend on the extraction of some features from a specific channel in color spaces (i.e., RGB images for generating an automated classification system [11,12]; brown area percentage in CIE L * a * b * [13]; ripening color index from the channels a * b * from CIE L * a * b * [14]; fuzzy system of images in two color spaces, HSV and CIE L * a * b * [15]). In all these cases, a three-channel image representation is needed.…”

Section: Texture Analysis Computing the Homogeneity Criteriamentioning

confidence: 99%

Nondestructive Quantification of the Ripening Process in Banana (Musa AAB Simmonds) Using Multispectral Imaging

et al. 2019

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The ripening process in bananas causes the waste of a significant part of the production of this fruit. The aim of this research is to find a new technique useful for identifying, registering, and quantifying the ripening process of a banana (Musa AAB Simmonds) at the seventh stage of the growing process. This quantification is proposed with a nondestructive technique based on processing multispectral images. This experiment used a set of multispectral imagery registered in a range of 270-1000 nm (from UV to IR) with the aid of a monochromatic camera and a set of 10 optical filters. Multispectral images were analyzed with three different techniques: Fourier fractal analysis, Hotelling transform, and homogeneity texture analysis based on cooccurrence matrix. First, a characteristic index was computed for each technique for a daily set of multispectral imagery. These indexes are slope index, for Fourier fractals; the average of the computed eigenvalues, with Hotelling transform; and the texture homogeneity value. These indexes were evaluated using the behavior of the resulting graphs for a seven-day period, being preferred those graphs with a tendency of decreasing values. Finally, the repeatability of each technique was evaluated by reproducing similar values for each day during the evaluation period. These three methods will be compared in this article in order to select the one with the best performance for measuring the ripening process in bananas. The obtained results show that it is possible to effectively isolate the brown spots from the banana peel with the Hotelling transform by using only 2 optical filters: visible (410-690 nm) and Near-IR (820-910 nm). With the resulting spectral image fusion it was possible to effectively describe the evolution of the brown spots present in the ripening process through the texture homogeneity criteria.

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Particle Swarm Optimized Fuzzy Model for the Classification of Banana Ripeness

Cited by 47 publications

References 25 publications

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques

Applications of Photonics in Agriculture Sector: A Review

Nondestructive Quantification of the Ripening Process in Banana (Musa AAB Simmonds) Using Multispectral Imaging

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