Olive Fruits Recognition Using Neural Networks

Gatica, C. Gabriel; Best, Stanley; Ceroni, Jose; Lefranc, Gastón

doi:10.1016/j.procs.2013.05.053

Cited by 36 publications

(23 citation statements)

References 11 publications

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“…The used of ANN in the classification analysis of agrobased product as well as in other application has been widely used such as in the classification of Chinese rice seed varieties [11], apple grading [12], discrimination of rapeseed varieties [13], olive fruits recognition [14], detection of segmentation points of Arabic Words [15] and face recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system. This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.

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Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

show abstract

“…ANN and SVM are supervised learning algorithms that have the ability to learn from the data through an iterative training process and improve its performance after each iteration. An olive recognition method using neural networks was presented by Gatica et al (2013). The process of fruit recognition comprised of two stages: the first stage focused on deciding whether or not the candidate identified in the image corresponds to an olive fruit, the second stage focused on olives overlapping within the tree canopy.…”

Section: Pattern Recognition Approachesmentioning

confidence: 99%

A review of key techniques of vision-based control for harvesting robot

Zhao

Gong

Huang

et al. 2016

Computers and Electronics in Agriculture

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126

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“…En el caso de los olivos, y con el fin de lograr una óptima recolección de los frutos, Gatica et al (50) proponen el uso de redes neuronales artificiales para el reconocimiento de la fruta de los olivos, analizando las imágenes y evaluando el tamaño y el peso, características normalmente tenidas en cuenta en una inspecciona manual.…”

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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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Olive Fruits Recognition Using Neural Networks

Cited by 36 publications

References 11 publications

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

A review of key techniques of vision-based control for harvesting robot

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

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