Toward a new approach in fruit recognition using hybrid RGBD features and fruit hierarchy property

Rachmawati, Ema; Supriana, Iping; Khodra, Masayu Leylia

doi:10.1109/eecsi.2017.8239110

Cited by 18 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Light Detection and Ranging (LiDAR) is also used widely for classification of fruit and vegetable in agricultural environments [48]. Significant research has been reported upon the utilisation of Light Structured Sensors (LSS), which exploits the depth data along with colour, shape and texture details [49,69,70]. Classification of fruit and vegetable was initially studied for autonomous harvesting with robots [21].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…There are various applications of RGBD data for example classification, object tracking, surface matching, 3D modelling and pose recognition [89,90]. Numerous commodity sensors are commercially available for sensing the RGDB data [3,51,70] and are being studied. A detailed comparison of sensors in terms of features exploited for fruit and vegetable classification is presented in Table 2.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Approximately, 150 classes of fruit and vegetable have been identified in a rough internet survey in Australian supermarkets, none of the previous studies have discussed such a number of classes. Recent advanced commodity RGBD sensors are being used for object classification [70,221,222,223,224,225,226,227], which can also be used for more effective classification of fruit and vegetable.…”

Section: Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

149

View full text Add to dashboard Cite

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.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

149

View full text Add to dashboard Cite

show abstract

“…The vision-based classification of fruit and vegetables has been performed in many fields for a range of different applications. The most common applications include the classification of fruit or vegetables for automated harvesting in agricultural settings [18][19][20] or vision-based quality assessment of fruit or vegetables [21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

2020

View full text Add to dashboard Cite

The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of fruit and vegetables have been obtained using a prototype designed to simulate the supermarket environment, including the lighting conditions. The weight information is used to change the coarse classification of 15 classes down to three, which are further used in AdaBoost-based Convolutional Neural Network (CNN) optimisation for fine classification. The training samples for each coarse class are weighted based on AdaBoost optimisation, which are updated on each iteration of a training phase. Multi-class likelihood distribution obtained by the fine classification stage is used to estimate a final classification with a softmax classifier. GoogleNet, MobileNet, and a custom CNN have been used for AdaBoost optimisation, with promising classification results.

show abstract

“…The authors of [5] also used the nearest-neighbor classifier for fruit classification, but focused on the depth channel of RGBD (Red, Green, Blue, Depth) images. The use of hierarchical multi-feature classification and hybrid features made it possible to obtain better results for system accuracy among species of fruits, as well as their variety.…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

Rudnik

Michalski

2019

Applied Sciences

View full text Add to dashboard Cite

This study proposes a double-track method for the classification of fruit varieties for application in retail sales. The method uses two nine-layer Convolutional Neural Networks (CNNs) with the same architecture, but different weight matrices. The first network classifies fruits according to images of fruits with a background, and the second network classifies based on images with the ROI (Region Of Interest, a single fruit). The results are aggregated with the proposed values of weights (importance). Consequently, the method returns the predicted class membership with the Certainty Factor (CF). The use of the certainty factor associated with prediction results from the original images and cropped ROIs is the main contribution of this paper. It has been shown that CFs indicate the correctness of the classification result and represent a more reliable measure compared to the probabilities on the CNN outputs. The method is tested with a dataset containing images of six apple varieties. The overall image classification accuracy for this testing dataset is excellent (99.78%). In conclusion, the proposed method is highly successful at recognizing unambiguous, ambiguous, and uncertain classifications, and it can be used in a vision-based sales systems in uncertain conditions and unplanned situations.

show abstract

Toward a new approach in fruit recognition using hybrid RGBD features and fruit hierarchy property

Cited by 18 publications

References 25 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

Contact Info

Product

Resources

About