Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles

Bişğin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G.; Wu, Leihong; Liu, Zhichao; Barnes, Amy; Langley, Darryl A.; Pava‐Ripoll, Monica; Vyas, Himansu J.; Xu, Joshua

doi:10.1038/s41598-018-24926-7

Cited by 86 publications

(61 citation statements)

References 39 publications

(50 reference statements)

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“…Three metrics including sensitivity, specificity, and precision were used to assess classification performance besides accuracy as described by Bisgin et al (). Almost all tested algorithms resulted the greatest metrics for E .…”

Section: Discussionmentioning

confidence: 99%

“…The classification success of ANN, kNN, and SVM was evaluated in terms of accuracy criteria. For a two‐class prediction problem, performance of algorithms were compared by calculating the value of sensitivity, precision and specificity as described by Bisgin et al (). There are four possible situations including true positive (TP), false negative (FN), true negative (TN), and false positive (FP).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Polat

Topalcengiz

Danyluk

2019

Journal of Food Safety

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The purpose of this study was to evaluate the performance of artificial intelligence tools for the prediction of Salmonella presence and absence in agricultural surface waters based on the population of microbiological indicators (total coliform, generic Escherichia coli, and enterococci) and physicochemical attributes of water (air and water temperature, conductivity, ORP, pH, and turbidity). Previously collected data set from six agricultural ponds monitored for two growing seasons were used for analysis. Classification algorithms including artificial neural networks (ANNs), the nearest neighborhood algorithm (kNN), and support vector machines (SVM) were trained and tested with a 539-point data set for optimum prediction accuracy. Classification accuracy performances were validated with data set (400 samples) collected from different agricultural surface water sources. All tested algorithms yielded the highest accuracy around 75 ± 1% for generic E. coli followed by enterococci (65 ± 5%) and total coliform (60 ± 10%). Classifiers calculated 6-15% higher accuracy ranging from 62 to 66% for turbidity than all other tested physicochemical attributes.Based on E. coli populations measured in other water sources, trained algorithms predicted the presence and absence of Salmonella with an accuracy between 58.15 and 59.23%. The classification performance of ANN, kNN, and SVM algorithms are encouraging for the prediction of Salmonella in agricultural surface waters.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Polat

Topalcengiz

Danyluk

2019

Journal of Food Safety

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“…There have been many published methods for detection of food contamination utilizing traditional machine learning algorithms (Bisgin et al., ; Ropodi, Panagou, & Nychas, ; Ravikanth, Jayas, White, Fields, & Sun, ). There are potential possibilities for deep learning to replace the traditional machine learning method to achieve better detection results for food contamination in different food production procedures.…”

Section: Food Contaminationmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

321

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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“…We compare the accuracy of ML and SVM in estimating coastal vegetation, in order to provide a steer to other studies. Specifically, we aimed to: From our knowledge of the literature [42,44,45,51], we hypothesized that SVM will have a better accuracy than ML in predicting LC classes using fused data. We also predicted from our knowledge of the region that mangrove area will have reduced in the delta due to deforestation, and Nipa Palm extent increased, over the study period.…”

Section: Introductionmentioning

confidence: 99%

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

2020

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Mangrove forests in the Niger Delta are very valuable, providing ecosystem services, such as carbon storage, fish nurseries, coastal protection, and aesthetic values. However, they are under threat from urbanization, logging, oil pollution, and the proliferation of the invasive Nipa Palm (Nypa fruticans). However, there are no reliable data on the current extent of mangrove forest in the Niger Delta, its rate of loss, or the rate of colonization by the invasive Nipa Palm. Here, we estimate the area of Nipa Palm and mangrove forests in the Niger Delta in 2007 and 2017, using 567 ground control points, Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat and the Shuttle Radar Topography Mission Digital Elevation Model 2000 (SRTM DEM). We performed the classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) methods. The classification results showed SVM (overall accuracy 93%) performed better than ML (77%). Producers (PA) and User’s accuracy (UA) for the best SVM classification were above 80% for most classes; however, these were considerably lower for Nipa Palm (PA—32%, UA—30%). We estimated a 2017 mangrove area of 801,774 ± 34,787 ha (±95% Confidence Interval) ha and Nipa Palm extent of 11,447 ± 7343 ha. Our maps show a greater landward extent than other reported products. The results indicate a 12% (7–17%) decrease in mangrove area and 694 (0–1304)% increase in Nipa Palm. Mapping efforts should continue for policy targeting and monitoring. The mangroves of the Niger Delta are clearly in grave danger from both rapid clearance and encroachment by the invasive Nipa Palm. This is of great concern given the dense carbon stocks and the value of these mangroves to local communities for generating fish stocks and protection from extreme events.

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Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles

Cited by 86 publications

References 39 publications

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Application of Deep Learning in Food: A Review

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

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