An analysis of the performance of Artificial Neural Network technique for apple classification

Bhatt, Ashutosh Kumar; Pant, Durgesh; Singh, Richa

doi:10.1007/s00146-012-0425-z

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…Their results showed a low level of error in prediction which verified that the ANN model is effective in estimating apple quality (Bhat, Pant, & Singh, 2014). Determining and predicting of peach fruits injury during the cold storage was investigated using hyperspectral reflectance imaging and ANN method.…”

Section: Liter Ature Re Vie Wmentioning

confidence: 79%

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Razavi

Golmohammadi

Sedghi

et al. 2019

Food Science & Nutrition

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Bruises occur under both static and dynamic loadings when the imposed stress on fruit goes over the failure stress of the fruit tissue. Bruise damage is the main reason for fruit quality loss. In this study, the potential of artificial neural network (ANN), adaptive neuro‐fuzzy inference system (ANFIS), and multiple regression (MR) techniques to predict bruise volume propagation of pears during the storage time was evaluated. For this purpose, at first, the radius of curvature at loading region was obtained. Samples were divided into five groups and subjected to five force levels. Then, they were kept under storage conditions and at 7‐time intervals after loading tests, bruise volume was calculated using magnetic resonance imaging (MRI) and image processing techniques. Force, storage time, and radius of curvature at loading region were employed as input variables, and bruise volume (BV) was considered as output in the developed models. Multilayer perceptron (MLP) artificial neural network with three layers that includes an input layer (three neurons), two hidden layers (two and nine neurons), and one output layer was used. For the evaluation of models, three criteria (RMSE, VAF, and R2) were calculated. ANN and MR gave the highest and lowest correlation between predicted and actual values, respectively. These results indicate that the ANN techniques can be used to predict pear bruising propagation in storage time.

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Section: Liter Ature Re Vie Wmentioning

confidence: 79%

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Razavi

Golmohammadi

Sedghi

et al. 2019

Food Science & Nutrition

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“…In paper [9], the authors suggested a way to classify the sentimental reviews from a phone. They were organized into various categories of service, name, and functional standards.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentimental Analysis and Visualization of Food Reviews from Zomato using Tableau

Chugh¹

2020

IJATCSE

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In today's digital era where everybody has their own opinion and so can post various feedbacks regarding different products. Reviews are critical in nature and can affect the market value of a product. Amazon, Zomato, Quora, Twitter, etc . are few such platforms that provide space for product reviews. Since reviews are textual and diversified; a collective representation will help users to summarize their opinions. Honest food reviews and its analysis is still a challenge. Sentiment Analysis or opinion mining indicates the utilization of computational linguistics, textual analysis, biostatistics, and NLP (Natural Language Processing) to consistently recognize, skin, compute and grasp effective levels and abstract information. In this paper, we used the database of 'Zomato Pune' downloaded from Kaggle which is used as a basic dataset to summarize the food reviews by customers into 3 basic categories whether positive, negative or neutral based on the food being served at the restaurants. A visual analytical tool is being used linked with Tableau to give clear and interactive charts: Bar Chart, Word-Cloud, and Packed Bubble as visualization techniques. Charts will help users to compare multiple options interactively at the same time.

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“…Regarding apples, the grading procedure is being divided to four steps, which are: the images acquisition, their segmentation, their interpretation and finally the fruit classification (Leemans et al, 1999;Lu, 2004;Mann et al, 2005;Mendoza et al, 2011Mendoza et al, , 2012Mendoza et al, , 2014Bhat, 2014). For this purpose, Kazuhiro (1997) proposed a neural network consisted of two layers and five hidden neurons, aiming to sort San-Fuji apples into five colour and quality classes.…”

Section: Apples Gradingmentioning

confidence: 99%

Data mining and optimisation issues in the food industry

Vlontzos

Pardalos

2017

IJSAMI

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Data mining applications in the food industry has until now expressed in many ways, on both technical and economic terms. The most important methodologies being used are clustering, classification, feature selection and outlier detection. The techniques commonly used in data mining are artificial neural networks, decision trees, k-means type algorithms, genetic algorithms, nearest neighbour method, and rule induction. Successful case studies of the implementation of these methodologies are fruit and vegetable classification, with special focus on apples, citrus, strawberries, table olives, onions etc. the same rationale is being followed for the evaluation of processed foodstuff. Efficient solutions have been provided for wine classification, based on organoleptic characteristics, fish and meat classification, as well as robotic harvesting. Finally, applications on supply chain management and e-commerce have provided significant solutions on issues of monitoring and evaluation of dynamically changing datasets.

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An analysis of the performance of Artificial Neural Network technique for apple classification

Cited by 23 publications

References 8 publications

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Sentimental Analysis and Visualization of Food Reviews from Zomato using Tableau

Data mining and optimisation issues in the food industry

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