Automatic quality evaluation of fruits using Probabilistic Neural Network approach

Ashok, Vani; Vinod, D.

doi:10.1109/ic3i.2014.7019807

Cited by 33 publications

(10 citation statements)

References 7 publications

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“…It is concluded that with Gabor based global segmentation of near infra-red (NIR) apple images there is no need of local feature segmentation. The Gabor filter used can extract specific frequency components that can be used for segmentation [32,108]. Recently, Otsu based segmentation has been used for fruit and vegetable defect detection and a common limitation of holes generation for similar intensity level as background has been identified [39,40,57,81].…”

Section: Segmentationmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

154

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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Section: Segmentationmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

154

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“…El color como evaluación de calidad de las frutas es solo una característica, a la que se le pueden sumar otras, como lo plantean Ashok y Vinod (27); estos proponen el uso de un clasificador de red neuronal probabilístico, al cual se le ingresan como datos de entrada los atributos externos de la manzana: el color, el tamaño, la forma, la textura y la presencia de daños. Consideraron Ashok y Vinod 20 imágenes del color de la manzana sin daños y 45 imágenes de la manzana con daños; la red neuronal fue capaz de distinguir el cambio en las características de la fruta con una exactitud de 86,52 % y 88.33 %.…”

Section: )unclassified

Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

Figueredo-Ávila

Ricaurte

2016

Cien. Agri.

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ResumenLa aplicación de las Redes Neuronales Artificiales (RNA) y de la visión artificial tiene cada vez más acogida en la industria de productos alimenticios; estas técnicas priorizan la clasificación, el reconocimiento de patrones y la predicción de las cosechas y de los cambios físicos de sus productos. En este artículo se define el concepto de red neuronal y se describen sus principales características y modelos, y, por otro lado, se define el concepto de procesamiento de imágenes digitales y las diferentes etapas que lo componen. Complementariamente, se exponen las generalidades de la inspección de frutas (enfocada en Colombia) y sus técnicas. Finalmente, se especifican diferentes trabajos en los que se aplicaron técnicas de RNA y visión artificial en el campo de los productos alimenticios, dispuestos por áreas de aplicación, y se identifica de manera concluyente el impacto que estas dos técnicas tienen para la clasificación, el reconocimiento de patrones y la predicción en el sector de productos alimenticios.Palabras clave: inspección de alimentos; procesamiento de imágenes; reconocimiento de objetos; Redes Neuronales Artificiales (RNA). AbstractThe application of Artificial Neural Networks (ANNs) and artificial vision has received more and more acceptance in the food industry. These techniques prioritize the classification, pattern recognition, and prediction of the harvests and physical changes in the products. In order to understand the impact of these techniques, this article defines the concept of neural network and describes its main characteristics and models; and, on the other hand, defines the concept of digital imagery processing and its different stages. Complementarily, this review presents an overview of fruit inspection (focused on Colombia) and its techniques; and specifies and orders by application area different works in which ANNs techniques

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“…Asho and Vinod [7] proposed a system for quality evaluation of fruits which is based on neural network. In proposed system test has been done on apple fruit.…”

Section: Related Workmentioning

confidence: 99%

Recent Research Trends of Plant Disease Detection

2015

IJSR

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Diagnosis of plant disease is a task of identifying the disease in the leaf or fruit or vegetable. About 42% of the world's agriculture harvest is destroyed yearly by disease and pest. However, losses of harvest can be minimized and specific treatments can be applied if plant diseases are correctly identified early. Manual identification of disease in the plant is not only time consuming but also does not give accurate result. So, providing fast, automatic and accurate solution using image processing techniques can be a good realistic significance. Automatic identification of diseases using image processing techniques can be done by using five methods like Image Acquisition, Image Pre-processing, image segmentation, Feature extraction and classification. Over last few years, many research works are carrying out in this area. This paper provides a brief overview on methodology. Further, paper reviews recent research trends to identifying disease in the plant which is based on image processing techniques.

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Automatic quality evaluation of fruits using Probabilistic Neural Network approach

Cited by 33 publications

References 7 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

Recent Research Trends of Plant Disease Detection

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