Performance analysis of soft computing techniques for the automatic classification of fruits dataset

Rajasekar, L.; Sharmila, D.

doi:10.1007/s00500-019-03776-z

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…The average of the 10 results was used as an estimate of the algorithm's accuracy. All the training data were involved in modeling, which could well estimate the training accuracy of the models [40].…”

Section: Modeling and Model Validationmentioning

confidence: 99%

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Luo

Xiong

et al. 2023

Foods

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Inner-injury fragrant pears are easily prone to rot during storage. Discriminating inner injury in the Korla fragrant pear from the normal pear is difficult as the flesh may be injured while the peel of the fruit remains intact. This study demonstrated the recognition of inner-injury pears based on their electric characteristics to pick out the inner-injury pears before storage. The electrical parameters parallel equivalent capacitance, quality factor, parallel equivalent inductance, parallel equivalent resistance, complex impedance, and phase angle were measured using the fruit electrical characteristic detection instrument. Principal component analysis and correlation analysis were used to determine the characteristic parameters, connected with the qualitative value of the fragrant pear to establish three discrimination models. When the measurement frequency was 100 kHz, compared with the Naïve Bayes and K-nearest neighbor models, the Support Vector Machine model with the characteristic parameters of quality factor, parallel equivalent resistance, and phase angle performed best. The recognition accuracy of the test set was 92.00%, the precision was 92.41%, the recall was 97.33%, and the F1 score was 0.95. Therefore, the electrical characteristic technique effectively detected the inner injury of fragrant pears and provided a new way to distinguish the inner injury of fruits.

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Section: Modeling and Model Validationmentioning

confidence: 99%

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Luo

Xiong

et al. 2023

Foods

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“…Where x includes an additional variable, x 0 = 1, that provides a bias term w 0 , included in the w matrix. Some other of the methods mentioned in the previous section for regression have also al application or variation for classification purposes such as SVM [109,[111][112][113][114] and KNN [115]. Another method that has been implemented in soft metrology models is the use of decision trees, which have de advantage of being able to deal with categorical as well a numerical values [116].…”

Section: Classification Modelsmentioning

confidence: 99%

Soft metrology based on machine learning: a review

Vallejo

Espriella

Gómez-Santamaría

et al. 2019

Meas. Sci. Technol.

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Soft metrology has been defined as a set of measurement techniques and models that allow the objective quantification of properties usually determined by human perception such as smell, sound or taste. The development of a soft metrology system requires the measurement of physical parameters and the construction of a model to correlate them with the variables that need to be quantified. This paper presents a review of indirect measurement with the aim of understanding the state of development in this area, as well as the current challenges and opportunities; and proposes to gather all the different designations under the term soft metrology, broadening its definition. For this purpose, the literature on indirect measurement techniques and systems has been reviewed, encompassing recent as well as a few older key documents to present a time line of development and map out application contexts and designations. As machine learning techniques have been extensively used in indirect measurement strategies, this review highlights them, and also makes an effort to describe the state of the art regarding the determination of uncertainty. This study does not delve into developments and applications for human and social sciences, although the proposed definition considers the use that this term has had in these areas.

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“…Vegetable recognition by visual may be easy for individuals who deal with them every day but not for a computer. There are numbers of research have been made on the topic of plant and vegetable classification [5]- [10].…”

Section: Related Workmentioning

confidence: 99%

“…Reference [5] developed an automatic fruit classification technique by fusing three basic features of colour, shape and texture to characterise the objects. There is a total of nine features from the three categories.…”

Section: Related Workmentioning

confidence: 99%

Multi-feature Vegetable Recognition using Machine Learning Approach on Leaf Images

Nasharuddin¹

2019

IJATCSE

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Vegetables are one of the staple foods that are being consumed daily by Malaysians. With the abundance of the vegetables' type, there are a lot of lookalike vegetables which are from the same species but different type. One of the ways to distinguish the types is by looking at the leaves which are the most visible part of a vegetable. An automated vegetable recognition approach using the colour and shape features of the leaf images is being studied in this work. We focus on the vegetables that mostly consumed by Malaysian. The presented approach was tested on 300 leaf images from six different types of vegetables. Few machine learning classification techniques have been compared, and it was shown that Support Vector Machine technique is the best classifier in this work. The experiments showed that the vegetables can be recognised accurately, up to 95.7% using the Support Vector Machine when using both features were used. The study revealed that the proposed recognition approach can provide a reliable and faster way to automatically classify vegetables which are common in Malaysia.

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Performance analysis of soft computing techniques for the automatic classification of fruits dataset

Cited by 16 publications

References 21 publications

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Soft metrology based on machine learning: a review

Multi-feature Vegetable Recognition using Machine Learning Approach on Leaf Images

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