A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Tripathi, Mukesh Kumar; Maktedar, Dhananjay D.

doi:10.5120/ijca2018917336

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…While mango deterioration is inevitable, nondestructive methods provide a solution for assessing external characteristics to detect various types of defects during the handling and processing of mangoes. Identifying superficial weaknesses is vital, as it can significantly impact the quality and market value of mango fruits [3]. However, grading mangoes based on their external parameters presents challenges due to several factors.…”

Section: Introductionmentioning

confidence: 99%

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

D. Baswaraj

2023

IJRITCC

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Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution.

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Section: Introductionmentioning

confidence: 99%

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

D. Baswaraj

2023

IJRITCC

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Contrast Enhancement Using Optimum Threshold Selection

Rani

Agarwal

2020

International Journal of Software Innovation

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In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

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A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Cited by 2 publications

References 39 publications

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Contrast Enhancement Using Optimum Threshold Selection

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