Bacterial Infection Potato Tuber Soft Rot Disease Detection Based on Electronic Nose

Chang, Zhiyong; Lv, Jing; Qi, Hongyan; Ma, Yunhai; Chen, Donghui; Xie, Jin; Sun, Youhong

doi:10.1515/biol-2017-0044

Cited by 23 publications

(6 citation statements)

References 14 publications

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“…This technique is widely implemented in image classification and segmentation problems, demonstrating significantly higher accuracy compared to traditional query refinement schemes [ 76 ], in biological and other sciences to classify different objects, chemicals, and media. In particular, they are successfully used for odor recognition in EN [ 77 , 78 , 79 , 80 ].…”

Section: Technology Used For Results Analysismentioning

confidence: 99%

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Mamchur

Pekša

Clainche

et al. 2022

Sensors

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Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

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Section: Technology Used For Results Analysismentioning

confidence: 99%

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Mamchur

Pekša

Clainche

et al. 2022

Sensors

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show abstract

“…SVM obtained higher accuracy than radial basis function neural networks with an increase of 5.5%, 4.4%, 0.5%, 3.5%, 2.9% and 2.2%. The overall accuracy of radial basis function neural network and SVM were 80.6% and 83.85% respectively [24].…”

Section: Potatomentioning

confidence: 98%

Applications of electronic nose and machine learning models in vegetables quality assessment: A review

Anwar

Anwar²,

Murtaza

2023

2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T)

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Vegetables are an integral part of a balanced diet. They are good source of nutrition enriched with vitamins, mineral and antioxidants. They are prone to spoilage after harvesting without proper storage. Their quality can be determined by chemical analysis, high-performance liquid chromatography and near-infrared detection method. These methods are time taking, required a high cost equipment and trained person to perform. This article discussed the basic concept of electronic nose and machine learning. Electronic nose is used to detect gases excreted from vegetables and produced electronic signals. Machine learning is trained on these signals and predict the quality of vegetables. This paper presents the application of an electronic nose system with machine learning models. It is studied that this method is a cost-effective, portable and potential upcoming technique to overcome quality issues in vegetables.

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“…Some studies have used electronic noses for the early detection of rotting potatoes. Zhiyong Chang et al predicted potatoes with different decay proportions in the laboratory and the average accuracy was up to 83.8% [24]. E. Biondi et al used a commercial electronic nose to detect potatoes with different storage scales and completed a visualization of normal potatoes and diseased potatoes through PCA [25].…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

Lin,

Wei,

Chen

et al. 2024

Sensors

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The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

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Bacterial Infection Potato Tuber Soft Rot Disease Detection Based on Electronic Nose

Cited by 23 publications

References 14 publications

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Applications of electronic nose and machine learning models in vegetables quality assessment: A review

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

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