Summary on fruit identification methods: A literature review

Fang-yuan, Liu; Snetkov, Leonid; Lima, Dimas

doi:10.2991/essaeme-17.2017.338

Cited by 10 publications

(9 citation statements)

References 35 publications

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“…Even with this improvement, the problem of exponential growth with the number of parts persists, which makes it unpractical for the problem presented in this paper, which requires speed for on-line operation. However, these methods, with expensive devices, do not bring good results, because the accuracies are lower than 85% [4]. To solve this problem, later scholars begin to pay attention to image-based fruit classification for its cheap device and wonderful performance.…”

Section: Related Workmentioning

confidence: 99%

“…In the past, the fruit picking and processing is based on artificial methods, resulting in a large amount of waste of labor [4]. Recently, researchers tended to apply nearinfrared imaging, gas sensor, and high-performance liquid chromatography devices to scan the fruit.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, researchers tended to apply nearinfrared imaging, gas sensor, and high-performance liquid chromatography devices to scan the fruit. Nevertheless, those methods need expensive devices (different types of sensors) and professional operators, and their overall accuracies are commonly lower than 85% [4]. With the fast development of 4G communication and widespread popularity of various mobile Internet devices, people have generated a great quantity of images, sounds, videos, and other information, and image recognition technology has been gradually mature.…”

Section: Introductionmentioning

confidence: 99%

“…With the fast development of 4G communication and widespread popularity of various mobile Internet devices, people have generated a great quantity of images, sounds, videos, and other information, and image recognition technology has been gradually mature. Image-based fruit identification has attracted the attention of researchers since their cheap device and excellent performance [4]. The intelligent identification of fruit can be used not only in the picking stage of the early fruit, but also in the picking and processing stage in the later stage.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Xue

Liu

2020

Complex Intell. Syst.

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Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to extract the features of image. In the first part of the framework, an unsupervised method with a set of images is applied to pre-train the greedy layer-wised CAE. We use CAE structure to initialize a set of weights and bias of ADN. In the second part of the framework, the supervised ADN with the ground truth is implemented. The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Xue

Liu

2020

Complex Intell. Syst.

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“…Computer vision-based pattern recognition technology is capable of processing complex information automatically. Further fuzzy SVM provides a better result [2]. But, high High-quality datasets are essential for designing a good classifier.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning

Yogesh*,

Dubey,

Ratan

2019

IJEAT

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Machine learning-based classification and detection of surface defect of fruit involve manual feature identification and selection from input datasets. Deep learning discovers the useful features from the input data. This approach simplifies the training of the neural network and makes them faster. The selection of useful patterns from the fruit features results in better accuracy. The number of layers represents the depth of the model. Neural network provides learning to the model. As the dataset contains many features. It is obvious that all features are not relevant to the system. The proposed system learns from these features by identifying the pattern and select the relevant features. This is the most crucial phase of the machine learning to identify the appropriate features to make the system faster and accurate. In this paper, we propose solving fruit surface defect detection using Automated Machine Learning (AML). The outcome is the prediction of the fruit surface defect in terms of probability due to nutrient deficiency

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