Assessment of banana fruit maturity by image processing technique

Prabha, D.; Kumar, J. Satheesh

doi:10.1007/s13197-013-1188-3

Cited by 138 publications

(50 citation statements)

References 8 publications

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“…Due to their abundant nutritional elements, bananas play a key role in the human diet, and they are the fourth most important food crop worldwide [1]. China is one of the main banana-growing and producing countries.…”

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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“…Surya Prabha et al (2013) [19],conducted a research for the banana fruit maturity assessment through image processing and the analysis of variance between all maturity stages based on features, length of major axis, length of minor axis, perimeter, area. The obtained results indicate that the mean color intensity and area features are key parameters in predicting the banana maturity.…”

Section: Color Geometry and Shapementioning

confidence: 99%

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

2019

International Journal of Innovative Research in Electronics and

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Soybean, the most popular golden bean of America, is widely known for its fat free food products. Richness in Protein makes it one of the best suggested meals which can be consumed in the form of pulses, oil, food for animals etc. The quality of such products is mainly dependent on the quality of Soybean procured as fresh farm produce. Governments and regional authorities have already defined the standards for quality assessment and grading of Soybean, which are meant to be followed in the commercial market while trading. Presently, visual inspection is the preferred way to conduct physical quality assessment of Soybean and it is performed by an expert person, at the time of procurement of Soybean based on the standards recommended by the buying authority. Physical parameters of Soybean kernel like color, growth corresponding to size, damage, fungi/ disease as well as mixing of other material/ objects in soybean sample influence the grade of that sample during quality assessment. Dependency of this process on human expert usually harms the accuracy in assessment. Therefore, an automated machine is desired to be a suitable solution to address this issue and can benefit in terms of increased accuracy, reliability, and reduced response time. Worldwide researchers are working on designing such automated systems for different type of fruits, grains etc. However, in case of Soybean, up till now we are successful in cleaning, sorting, color detection, and also, through various image processing techniques and algorithms researchers could detect the anomalies present in grain sample. But an automated system for the quality assessment and grading of Soybean according to an International Standard is yet to be implemented. In this paper, we propose a two-stage model for the quality assessment of Soybean; first stage focuses on image processing techniques like Image acquisition, preprocessing and feature extraction Likewise the second stage works on classification of Soybean kernels and sample grading with the help of a machine learning technique on the basis of an International Standard.

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“…Color variation in uncoated and coated fruits emerged on the ninth day: coated and uncoated fruits were classified as level 7 (yellow slightly mottled brown) and level 8 (yellow with big brown areas), respectively (supplementary material). Classification based on the color chart is not precise, although classifier algorithms have been developed for an automatic decision (Prabha and Kumar 2015), the grading levels is still subjective.…”

Section: Skin Colormentioning

confidence: 99%

Evaluation of the turmeric dye extraction residue in the formation of protective coating on fresh bananas (Musa acuminata cv. ‘Maçã’)

et al. 2018

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We evaluate the flour obtained from the residue of turmeric dye extraction, designated turmeric flour, as a protective coating to extend banana () shelf life. The coating formulation consists of 6 g of turmeric flour/100 g of solution and 30 g of glycerol/100 g of turmeric flour, produced by immersion. We also investigate how the coating affects banana weight loss, firmness, pH, titratable acidity, soluble solids and reducing sugars contents, and peel color along 15 days of storage at 27 ± 2 °C and 65% RH. Coatings based on turmeric flour display good antioxidant activity, which is attributed to the presence of curcuminoids, mainly curcumin. These coatings delay weight loss, color development, and firmness reduction, and they afford lower acidity and sugars content in coated bananas as compared to control bananas. Compared to uncoated samples, turmeric flour-based coatings extend the original characteristics of bananas stored at 27 °C by 3 days.

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Assessment of banana fruit maturity by image processing technique

Cited by 138 publications

References 8 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

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

Evaluation of the turmeric dye extraction residue in the formation of protective coating on fresh bananas (Musa acuminata cv. ‘Maçã’)

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