Valley based multiclass thresholding for color image segmentation

Pattnaik, Tapaswini; Kanungo, P.

doi:10.1109/mami.2017.8307891

Cited by 2 publications

(1 citation statement)

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“…When making any kind of a vision system for a prototype, the image segmentation which is from algorithms is crucial. It shows how efficient the system can be since it is where the image is broken into parts and those segments are analyzed to get the needed data [18]. In doing image segmentation for this paper regarding pineapple maturity, the process aims to divide the image into regions and spot specific details with properties such as texture, color, and gray level, and this process is considered as a key step in doing image analysis for classifying [19].…”

Section: Introductionmentioning

confidence: 99%

Pineapple maturity classifier using image processing and fuzzy logic

Arboleda

Jesus

Tia

2021

IJ-AI

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This paper describes the development of a prototype using an image processing system for extracting features and fuzzy logic for classifying the maturity of pineapple fruits depending on the colors of its scales. The standards that the system used are from Philippine National Standards for fresh fruits-pineapple for the 'queen' variant. The prototype automatically classified the maturity of queen pineapple variant grown in Munting Ilog, Silang, Cavite, Philippines. Data gathered are from the images loaded into the system using a camera unit under a controlled environment. The images loaded consist of the three faces of the pineapple sample, each with 120-degree coverage to capture the whole 360-degree view of the scale. The images then are sent to the system of the prototype where the features of the images are segmented based on the RGB color reduction. By using the fuzzy logic classifier, the obtained experimental results showed 100% accuracy for both the unripe and overripe maturity and 90% accuracy for the under-ripe and ripe maturity classification. The results obtained show that the developed image processing algorithm and the fuzzy-logic-based classifier could be used as an accurate and effective tool in classifying the maturity of pineapples.

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Section: Introductionmentioning

confidence: 99%