Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning
Codling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from our group demonstrated the effectiveness of hyp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/4/839 |
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| author | Nader Ekramirad Alfadhl Y. Khaled Kevin D. Donohue Raul T. Villanueva Akinbode A. Adedeji |
| author_facet | Nader Ekramirad Alfadhl Y. Khaled Kevin D. Donohue Raul T. Villanueva Akinbode A. Adedeji |
| author_sort | Nader Ekramirad |
| collection | DOAJ |
| description | Codling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from our group demonstrated the effectiveness of hyperspectral imaging (HSI) and acoustic methods as suitable techniques for nondestructive CM infestation detection and classification in apples. However, both have limitations that can be addressed by the strengths of the other. For example, acoustic methods are incapable of detecting external CM symptoms but can determine internal pest activities and morphological damage, whereas HSI is only capable of detecting the changes and damage to apple surfaces and up to a few mm inward; it cannot detect live CM activity in apples. This study investigated the possibility of sensor data fusion from HSI and acoustic signals to improve the detection of CM infestation in apples. The time and frequency domain acoustic features were combined with the spectral features obtained from the HSI, and various classification models were applied. The results showed that sensor data fusion using selected combined features (mid-level) from the sensor data and three apple varieties gave a high classification rate in terms of performance and reduced the model complexity with an accuracy up to 94% using the AdaBoost classifier, when only six acoustic and six HSI features were applied. This result affirms that the sensor fusion technique can improve CM infestation detection in pome fruits such as apples. |
| first_indexed | 2024-03-11T05:20:36Z |
| format | Article |
| id | doaj.art-1efff40964744bfbb96c780aa544da7f |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T05:20:36Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-1efff40964744bfbb96c780aa544da7f2023-11-17T17:54:12ZengMDPI AGAgriculture2077-04722023-04-0113483910.3390/agriculture13040839Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine LearningNader Ekramirad0Alfadhl Y. Khaled1Kevin D. Donohue2Raul T. Villanueva3Akinbode A. Adedeji4Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USADepartment of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USADepartment of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USADepartment of Entomology, University of Kentucky, Princeton, KY 42445, USADepartment of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USACodling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from our group demonstrated the effectiveness of hyperspectral imaging (HSI) and acoustic methods as suitable techniques for nondestructive CM infestation detection and classification in apples. However, both have limitations that can be addressed by the strengths of the other. For example, acoustic methods are incapable of detecting external CM symptoms but can determine internal pest activities and morphological damage, whereas HSI is only capable of detecting the changes and damage to apple surfaces and up to a few mm inward; it cannot detect live CM activity in apples. This study investigated the possibility of sensor data fusion from HSI and acoustic signals to improve the detection of CM infestation in apples. The time and frequency domain acoustic features were combined with the spectral features obtained from the HSI, and various classification models were applied. The results showed that sensor data fusion using selected combined features (mid-level) from the sensor data and three apple varieties gave a high classification rate in terms of performance and reduced the model complexity with an accuracy up to 94% using the AdaBoost classifier, when only six acoustic and six HSI features were applied. This result affirms that the sensor fusion technique can improve CM infestation detection in pome fruits such as apples.https://www.mdpi.com/2077-0472/13/4/839apples (<i>Malus domestica</i>)codling mothsensor fusionhyperspectral imageacousticmachine learning |
| spellingShingle | Nader Ekramirad Alfadhl Y. Khaled Kevin D. Donohue Raul T. Villanueva Akinbode A. Adedeji Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning Agriculture apples (<i>Malus domestica</i>) codling moth sensor fusion hyperspectral image acoustic machine learning |
| title | Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning |
| title_full | Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning |
| title_fullStr | Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning |
| title_full_unstemmed | Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning |
| title_short | Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning |
| title_sort | classification of codling moth infested apples using sensor data fusion of acoustic and hyperspectral features coupled with machine learning |
| topic | apples (<i>Malus domestica</i>) codling moth sensor fusion hyperspectral image acoustic machine learning |
| url | https://www.mdpi.com/2077-0472/13/4/839 |
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