A Comparative Approach of Segmentation Methods Using Thermal Images of Apple

Yogesh, .; Dubey, Ashwani Kumar; Arora, Rajeev Ratan

doi:10.1109/icrito.2018.8748437

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…It was revealed that the distribution of image parameter values was signi cantly different between all pineapple cultivars. Considering the difference in the fruit cultivar, the temperature differences were attributed to the selected features of the thermal images (Yogesh et al 2018). The changes of image parameters showed the pixel distribution based on the temperature mapping attained at the surface of the pineapples for different fruit cultivars.…”

Section: Feature Selectionmentioning

confidence: 99%

Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms

Ali

Hashim

Aziz

et al. 2022

Preprint

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The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars i.e. MD2, Morris, and Josapine which were subjected at different storage temperatures i.e. 5, 10, and 25 °C and a relative humidity of 85 to 90 %. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and Naïve Bayes to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100 %. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars which could enhance the grading and sorting processes of the fruit.

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Section: Feature Selectionmentioning

confidence: 99%

Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms

Ali

Hashim

Aziz

et al. 2022

Preprint

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show abstract

“…A good technique of segmentation that helps to sort quality fruit in comparison with vision. This study develops a comparison technique for the detection of defective apples using several segmentation methods 7) .…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Based Quality Assessment of Guava Fruit

Patil¹,

Shekhawat²

2022

Evergreen

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Merchants and food services are very competitive, and food manufacturers are under considerable pressure not to increase end-product costs when production costs go up. External pressures causing physical texture changes or chemical color, smell, and taste alterations induce fruit tissue injury. To examine this, a novel technique is proposed to identify surface and subsurface flaws utilizing a thermal picture and ordinary digital image concerning the original guava. This study discovered thermal data for thermal image capturing on the guava samples stored in the atmosphere with a FLIR-C2 thermal camera. A systematic Image processing approach was employed to distinguish the injured tissues of the fruits that were unaffected. The total result of the identification of thermal blushes is 95 %, whereas the result of standard image processing is 62.5 %. Theory permitted to differentiate the sound from the defected fruit scorched by birds or insects in creating food processing safety policies, developing the passive thermographic method.

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Application of Machine Learning for Analysis of Fruit Defect: A Review

Tulli

Yogesh

2023

Lecture Notes in Electrical Engineering

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A Comparative Approach of Segmentation Methods Using Thermal Images of Apple

Cited by 6 publications

References 12 publications

Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms

Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms

Artificial Neural Based Quality Assessment of Guava Fruit

Application of Machine Learning for Analysis of Fruit Defect: A Review

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