Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Ismail, A. H.; Idris, Mohd Yamani Idna; Ayub, Mohamad Nizam Bin; Por, Lip Yee

doi:10.3390/sym11101194

Cited by 8 publications

(2 citation statements)

References 75 publications

(150 reference statements)

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“…Efforts to date have focused on accessing the quality of foods using vision-based methods. For example, Ismail et al have contributed an Apple-NDDA dataset [ 160 ] that consists of defective and non-defective apple images for food quality assessment.…”

Section: Existing and Potential Applications Of Vision-based Methods For Food Recognition In Healthcarementioning

confidence: 99%

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Tahir

Loo

2021

Healthcare

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Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.

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Section: Existing and Potential Applications Of Vision-based Methods For Food Recognition In Healthcarementioning

confidence: 99%

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Tahir

Loo

2021

Healthcare

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“…Duo the drawback of deeplearning or 3D imaging, some researchers still focus on traditional image segmentation methods that make up of image features and machine learning classier [9,15,[24][25][26][27][28]. Montalvo et al [29] successfully segmented maize crops from weeds using combinations of RGB color components derived from Principal Component Analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

2020

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Apples are one of the most kind of important fruit in the world. China has been the largest apple producing country. Yield estimating, robot harvesting, precise spraying are important processes for precise planting apples. Image segmentation is an important step in machine vision systems for precision apple planting. In this paper, an apple fruit segmentation algorithm applied in the orchard was studied. The effect of many color features in classifying apple fruit pixels from other pixels was evaluated. Three color features were selected. This color features could effectively distinguish the apple fruit pixels from other pixels. The GLCM (Grey-Level Co-occurrence Matrix) was used to extract texture features. The best distance and orientation parameters for GLCM were found. Nine machine learning algorithms had been used to develop pixel classifiers. The classifier was trained with 100 pixels and tested with 100 pixels. The accuracy of the classifier based on Random Forest reached 0.94. One hundred images of an apple orchard were artificially labeled with apple fruit pixels and other pixels. At the same time, a classifier was used to segment these images. Regression analysis was performed on the results of artificial labeling and classifier classification. The average values of Af (segmentation error), FPR (false positive rate) and FNR (false negative rate) were 0.07, 0.13 and 0.15, respectively. This result showed that this algorithm could segment apple fruit in orchard images effectively. It could provide a reference for precise apple planting management.

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Automated quality inspection of baby corn using image processing and deep learning

Wonggasem,

Chakranon,

Wongchaisuwat

2024

Artificial Intelligence in Agriculture

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Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Cited by 8 publications

References 75 publications

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

Automated quality inspection of baby corn using image processing and deep learning

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