Classification of pesticide residues in cabbages based on spectral data
Pesticide residue in leafy vegetables like a cabbage can cause harmful effects to consumers. Thus, early detection and classification of pesticide residue could help consumers to choose residue-free cabbages. This research was performed to evaluate the performance of different classification methods...
| Main Authors: | , , , , , |
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| Format: | Article |
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Farm Machinery Industrial Research Corp.
2021
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| _version_ | 1825937707175510016 |
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| author | Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani Mat Nawi, Nazmi Janius, Rimfiel Mazlan, Norida Ta, Te Lin Li, Ta Chen |
| author_facet | Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani Mat Nawi, Nazmi Janius, Rimfiel Mazlan, Norida Ta, Te Lin Li, Ta Chen |
| author_sort | Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani |
| collection | UPM |
| description | Pesticide residue in leafy vegetables like a cabbage can cause harmful effects to consumers. Thus, early detection and classification of pesticide residue could help consumers to choose residue-free cabbages. This research was performed to evaluate the performance of different classification methods to classify spectral data collected from 60 pesticide-free cabbage samples. Deltamethrin pesticide was sprayed on the samples at different dilution concentrations namely pesticide-free (PF), pesticide-low (PL), pesticide-medium (PM) and pesticide-high (PH). The spectral data of the cabbages was recorded using a spectrometer with an effective wavelength in the range of 400 to 1000 nm. The concentration of the pesticide residues in each cabbage sample
was quantified using a gas chromatography with an electron detector (GC-ECD). Three classification methods investigated in this study were artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR). The results show that LR, SVM and ANN yielded excellent classification accuracies of 95, 88 and 87%, respectively. This study revealed that the spectroscopic measurement coupled with classification methods are promising technique for detecting and classifying pesticides residues in cabbage samples. |
| first_indexed | 2024-03-06T11:03:36Z |
| format | Article |
| id | upm.eprints-96399 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:03:36Z |
| publishDate | 2021 |
| publisher | Farm Machinery Industrial Research Corp. |
| record_format | dspace |
| spelling | upm.eprints-963992023-02-13T01:08:12Z http://psasir.upm.edu.my/id/eprint/96399/ Classification of pesticide residues in cabbages based on spectral data Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani Mat Nawi, Nazmi Janius, Rimfiel Mazlan, Norida Ta, Te Lin Li, Ta Chen Pesticide residue in leafy vegetables like a cabbage can cause harmful effects to consumers. Thus, early detection and classification of pesticide residue could help consumers to choose residue-free cabbages. This research was performed to evaluate the performance of different classification methods to classify spectral data collected from 60 pesticide-free cabbage samples. Deltamethrin pesticide was sprayed on the samples at different dilution concentrations namely pesticide-free (PF), pesticide-low (PL), pesticide-medium (PM) and pesticide-high (PH). The spectral data of the cabbages was recorded using a spectrometer with an effective wavelength in the range of 400 to 1000 nm. The concentration of the pesticide residues in each cabbage sample was quantified using a gas chromatography with an electron detector (GC-ECD). Three classification methods investigated in this study were artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR). The results show that LR, SVM and ANN yielded excellent classification accuracies of 95, 88 and 87%, respectively. This study revealed that the spectroscopic measurement coupled with classification methods are promising technique for detecting and classifying pesticides residues in cabbage samples. Farm Machinery Industrial Research Corp. 2021 Article PeerReviewed Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani and Mat Nawi, Nazmi and Janius, Rimfiel and Mazlan, Norida and Ta, Te Lin and Li, Ta Chen (2021) Classification of pesticide residues in cabbages based on spectral data. AMA-Agricultural Mechanization in Asia Africa and Latin America, 52 (3). pp. 4063-4074. ISSN 0084-5841 https://www.researchgate.net/publication/357683513_Classification_of_Pesticide_Residues_in_Cabbages_based_on_Spectral_Data_Corresponding_Author_2 |
| spellingShingle | Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani Mat Nawi, Nazmi Janius, Rimfiel Mazlan, Norida Ta, Te Lin Li, Ta Chen Classification of pesticide residues in cabbages based on spectral data |
| title | Classification of pesticide residues in cabbages based on spectral data |
| title_full | Classification of pesticide residues in cabbages based on spectral data |
| title_fullStr | Classification of pesticide residues in cabbages based on spectral data |
| title_full_unstemmed | Classification of pesticide residues in cabbages based on spectral data |
| title_short | Classification of pesticide residues in cabbages based on spectral data |
| title_sort | classification of pesticide residues in cabbages based on spectral data |
| work_keys_str_mv | AT chemohammadishkandarelrahiminchedinimaryani classificationofpesticideresiduesincabbagesbasedonspectraldata AT matnawinazmi classificationofpesticideresiduesincabbagesbasedonspectraldata AT janiusrimfiel classificationofpesticideresiduesincabbagesbasedonspectraldata AT mazlannorida classificationofpesticideresiduesincabbagesbasedonspectraldata AT tatelin classificationofpesticideresiduesincabbagesbasedonspectraldata AT litachen classificationofpesticideresiduesincabbagesbasedonspectraldata |