Grading of Apples Using Multiple Features

Sunil, Smrithy R.; Anita, H. B.; Renupriya, P.

doi:10.1007/978-981-16-7952-0_18

Cited by 1 publication

(2 citation statements)

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“…Naive Bayes, Random Forest, and MLP classifiers processed images, extracting spatial- and frequency-based data. Utilizing methods like Fourier transform and discrete cosine transform, the average accuracy reached 78.47% …”

Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

“…Utilizing methods like Fourier transform and discrete cosine transform, the average accuracy reached 78.47%. 42 These studies on digital image processing for apple sorting reveal a diverse range of methodologies and techniques employed to enhance sorting accuracy and efficiency. Researchers have explored the use of various imaging sensors, such as NIR and UV cameras, coupled with advanced algorithms such as SVMs, CNNs, and mechatronic systems.…”

Section: Apple Sorting Based On Digital Image Processingmentioning

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: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%