Machine learning applications to non-destructive defect detection in horticultural products

Nturambirwe, Jean Frederic Isingizwe; Opara, Umezuruike Linus

doi:10.1016/j.biosystemseng.2019.11.011

Cited by 101 publications

(58 citation statements)

References 144 publications

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“…The accuracy of the model using RBF polynomial kernel was confirmed to be 92.30%. This can be considered a valid result, since this accuracy is higher compared to similar research, despite lack of data [ 45 , 46 ]. From these results, the SVM algorithm-based impact damage state classification method presented in this study is reasonable.…”

Section: Resultsmentioning

confidence: 51%

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Won

Woo

et al. 2020

Materials

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Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.

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Section: Resultsmentioning

confidence: 51%

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Won

Woo

et al. 2020

Materials

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“…The simplest ANN is a multi-layer perceptron composed of an input layer, hidden layer, and output layer ( Sanz et al., 2016 ). Neural networks have proven their effectiveness in pattern generation and classification whereby the feed forward neural networks have proven to be the most widely applied neural network ( Nturambirwe and Opara, 2020 ). Back propagation is the method used for training the neural network.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…With the advancement in computing system, improved hardware and software of the HSI system should be developed for rapid processing of hyperspectral images. Currently, the machine learning algorithms has a narrow specific application in food which requires standardization for wider applications ( Nturambirwe and Opara, 2020 ).…”

Section: Future Trends and Scope For Developmentmentioning

confidence: 99%

“…It is very difficult to choose an algorithm that will solve most of the problems, hence choosing an appropriate algorithm is very important for effectiveness of the model. Hence, future work is required to build a framework for algorithms that can be recommended for specific applications ( Nturambirwe and Opara, 2020 ).…”

Section: Future Trends and Scope For Developmentmentioning

confidence: 99%

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Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

229

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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“…Deep learning (DL) method is a deep structure algorithm emerging in recent years, which can learn the deep features of image data. It has made brilliant achievements in image classification field et al [24,25] , and it's application o intelligent recognition in precision agriculture is also on the rise [26][27][28]. By introducing DL into the field of intelligent identification of agricultural diseases and improving its identification accuracy, the practicability of the deep learning method in the field of intelligent diagnosis of diseases and pests will be further improved.…”

Section: Introductionmentioning

confidence: 99%

Corn Diseases Recognition Method Based on Multi-feature Fusion and Improved Deep Belief Network

Fan

Zhou

Yan

et al. 2021

Preprint

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The automatic monitoring timely and accurately of crop diseases has become an important research field in the process of intelligent management of precision agriculture. Aiming at the low application rate of current crop disease identification methods in real field environment, a disease identification method based on multi-feature fusion and improved deep belief network is proposed. We Obtained representative samples in the field, and augmented the data set. K-means clustering segmentation and morphological corrosion processing were utilized to obtain segmentation maps with clear boundaries and low noise. Then color features, shape features and textures of disease images were extracted and they were fused to normalize as input data. A corn disease recognition model based on deep belief network was designed, using labeled and unlabeled dual hidden layer network structure to study the DBN hidden layer node combination. We obtained the optimal hidden layer node number combination method for disease classification: [26,85,29,4], and the accuracy of DBN was 92.79%. On this basis, the deep belief network recognition model was optimized by particle swarm optimization algorithm. The experiment indicated that recognition effect using multi-feature fusion as input vectors was better than a single feature. The improved PSO-DBN had a faster convergence speed than the standard DBN, it has a recognition accuracy of 3.86% higher than that of DBN. Compared with state-of-the-art methods including SVM, ANN and CNN models, the proposed deep belief network can effectively dig deep into the digital features of disease areas or lesions and has the best performance, which could meet the needs of intelligent identification of field diseases.

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Machine learning applications to non-destructive defect detection in horticultural products

Cited by 101 publications

References 144 publications

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Corn Diseases Recognition Method Based on Multi-feature Fusion and Improved Deep Belief Network

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