New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques

Hassan, Nashat M. Hussain; Nashat, Ahmed A.

doi:10.1007/s11045-018-0573-5

Cited by 38 publications

(14 citation statements)

References 24 publications

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“…A homogeneity is measured by considering an adaptive threshold based on defect area pixel intensity variance. Significant accuracy rate has been presented but it can be identified that the approach has a high class dependency [43]. An ROI based multi-feature fusion has been performed by fusing HOG, LBP and GaborLBP for texture representation in [7].…”

Section: Average Pooling Max Pooling Spatial Pyramid Pooling (Spp)mentioning

confidence: 99%

“…In general, machine learning based colour representation is used for classification of objects from images or videos [182]. Colour of a [178] 2012 Vegetable Classification kurtosis and skewness 95.00% [77] 2013 Mixed fruit Quality assessment Curvelet-based statistical feature 91.42% [179] 2015 Mixed fruit Classification Wavelet Entropy 89.50% [29] 2016 Mixed fruit Classification Local relative phase binary patterns (LRPBP) 95.83% [176] 2017 Apple Recognition Grey-scale difference with statistical feature 98.08% [138] 2017 Grapevine bud Detection SFIT with BOF 96.50% [56] 2017 Mango Segmentation Dense SIFT-based histogram visual word 88.00% [137] 2017 Mixed fruit Recognition Mean, Standard Deviation, Skewness and Kurtosis 83.30% [177] 2018 Mixed fruit Detection Fused HOG, LBP, and GaborLBP 98.50% [7] 2018 Olive fruit Quality assessment THMT based threshold comparison 100.00% [43] (a) (b) (c) fruit or vegetable is governed by physical, biochemical and microbial changes during ripening and growth. However, the photometric changes i.e.…”

Section: Colour Feature Descriptorsmentioning

confidence: 99%

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A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

155

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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: Average Pooling Max Pooling Spatial Pyramid Pooling (Spp)mentioning

confidence: 99%

Section: Colour Feature Descriptorsmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

155

View full text Add to dashboard Cite

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“…Then we select the appropriate threshold to extract mold and green area by graying the picture and using threshold segmentation [22]. Then the closed morphological operation is carried out to remove the spots.…”

Section: Visual Feature Extraction Methodsmentioning

confidence: 99%

On Lemon Defect Recognition with Visual Feature Extraction and Transfers Learning

He¹,

Zhu²,

Wang³

et al. 2021

JDAIP

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Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (i.e., green and mold defects). First, the data enhancement and brightness compensation techniques are used for data prepossessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geometry Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as K-nearest Neighbor (KNN) and Support Vector Machine (SVM). Results show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.

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“…Maturities, ripeness, quality and diseases of the fruits have been subject of interest for many studies and included www.ijacsa.thesai.org different types of fruits such as Grape, Apple, Tomatoes, Cucumber, Orange, Palm oil fruits and many other fruits [21][22][23][24][25]. Studies on Olive fruit can be divided into classification the quality of olive fruits for olive table (pickled olive) [26,27], external damage and diseases of olive fruit [28,29] and olive fruit ripping time for olive oil [30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Olive Oil Ripping Time Prediction Model based on Image Processing and Neural Network

Alkhasawneh¹

2021

IJACSA

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The agriculture sector in Jordan depends very much on planting the olive trees. More than ten million of olive trees are planted in the Jordanian soil. Olive fruit are harvested for two purposes; either to produce oil or to produce olive table (pickled olive). Olive fruit harvesting time for extracting the oil from the olive fruit is crucial. Hence, harvesting the olive fruit on ripping time gives the best amount and quality of oil. It also, could lose 15% to 20% of multiple values because of harvesting time. Olive fruit ripping time is varied since it depends on the rainfall, temperature and cultivation. A system to predict the optimal time for harvesting olive fruit for producing oil only is introduced. It based one Digital Image Processing (DIP) and artificial intelligent neural network. Moreover, four features were extracted from the olive fruit image based on the red, green and blue colors. The proposed system tested olive fruits in three stages of ripping time; under ripping, on ripping and over ripping. The classification accuracy achieved in the three stages was 97.51% in under ripping stage 95.10% in ripping stage, and 96.12% in over ripping stage. The proposed system performance was 96.14%.

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New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques

Cited by 38 publications

References 24 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

On Lemon Defect Recognition with Visual Feature Extraction and Transfers Learning

Olive Oil Ripping Time Prediction Model based on Image Processing and Neural Network

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