Apple fruit sorting using novel thresholding and area calculation algorithms

Chithra, P. L.; Henila, Manickam

doi:10.1007/s00500-020-05158-2

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…The defective area calculation algorithm (DACA) demonstrated efficiency, with a 0.00612-s advantage over MATLAB's bwarea function in determining the defective area percentage. 13 In a recent study, apples were graded based on characteristics from photographs, including shape, size, and color, across seven varieties and diverse samples from Karnataka, India. Naive Bayes, Random Forest, and MLP classifiers processed images, extracting spatial-and frequency-based data.…”

Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

“…In order to boost classification success or speed up the classification process, it is crucial to develop a fruit size detection and grading system that functions quickly, inexpensively, and with greater accuracy. Currently, the fruit industry is very interested in using cutting-edge techniques such as hyperspectral imaging, near-infrared spectroscopy, mid-infrared spectroscopy, UV–vis, Raman spectroscopy, magnetic resonance imaging, X-ray computed tomography, optical and online detection, computer vision combined with neural networking, multivariant data analysis, and mathematical modeling (LDA, PLSDA, and SVM). ,,,, In this review, we are mainly highlighting the different techniques of apple classification along with their advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

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Apple sorting is a crucial step for their grading and determination of their commercial value. Sorting techniques are employed at several stages of production process, including harvest, storage, and packing. Manually apples are sorted and graded according to size and color, which is time-consuming, labor-intensive, and error-prone. Therefore, investigating nondestructive, precise, and effective methods of apple sorting is necessary to surmount the drawbacks connected with this. Technology advancements have led to the invention of several automated sorting techniques based on digital image processing, spectral and optical properties that are rapid and efficient compared to manual sorting. These technologies have advanced significantly for assessing the physical and nutritional quality of apples along with internal defects. In the present review, an overview of these advanced apple sorting methods is provided in detail. The contemporary techniques have improved apple sorting accuracy and efficiency while reducing labor costs and improving the quality. Computer vision systems can achieve high levels of accuracy and consistency by enabling nondestructive and quick assessment of many quality metrics, decreasing the subjectivity involved with human sorting. Spectral analysis and hyperspectral imaging have potential for determining interior characteristics like sugar content and ripeness. Robotics, machine learning, and sensor technologies can improve the efficiency, precision, and adaptability of sorting systems by learning from prior sorting data and adjusting to fresh variations in apple quality. In addition, sorting methods combining different techniques need to be developed to detect internal and external quality of apples.

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Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…(2) Image segmentation and classifiers based on traditional machine learning, such as K-means clustering algorithm-based methods [68][69][70][71][72][73][74][75], SVM (Support Vector Machine) algorithm-based methods [54,57,69,73,[76][77][78][79][80][81][82][83][84], KNN (K Nearest Neighbor) clustering algorithm-based methods [36,[85][86][87][88][89][90][91], AdaBoost (Adaptive Boosting) algorithm-based methods [62,[92][93][94][95][96][97][98][99], decision tree algorithm-based methods [100][101][102][103][104][105][106][107], and Bayesian algorithmbased methods [108]…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

Wang

et al. 2023

Agronomy

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The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning, this article summarizes and analyzes some representative methods. This article also demonstrates the current challenges and future potential developments. This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning.

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“…Therefore, increasing the performance of SVM without involving a deep learning algorithm is more challenging. Our approach is also different from [19]. In [19], the authors employed the Nave Bayes algorithm to classify apple fruit disease.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine with a firefly optimization algorithm for classification of apple fruit disease

Fawwaz Al Maki,

Jafar Makrufi

2022

matrik

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Fruit diseases became one of the serious problems that the farmer faced because it could threaten their economic outcome. The main focus of this study is apples. Apple fruit is very susceptible to disease, in general diseases that usually attack the apple are blotch apple, rot apple, and scab apple. In this study, the author is classifying these three apple diseases and normal apples. Classification is a process that we can do manually by human power, which costs a lot of fortune, takes a long time, and it's also very vulnerable to false identification. This study takes advantage of computer vision technology and machine learning to overcome the classification problem. By using the SVM method and parameter FA optimization algorithm, we can get the highest result only in the first experiment and also with 94% accuracy.

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Apple fruit sorting using novel thresholding and area calculation algorithms

Cited by 17 publications

References 19 publications

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

Support vector machine with a firefly optimization algorithm for classification of apple fruit disease

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