FT-IR absorbance data for early detection of oil palm fungal disease infestation
Basal stem rot (BSR) is a serious fungal disease in oil palm plantations which potentially could reduce the market share of palm oil for Malaysia. Due to the preventing great losses in production and reducing the use of chemicals, early detection of Ganoderma fungal infection is critical for managem...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Faculty of Engineering, Universiti Putra Malaysia
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/50659/ http://psasir.upm.edu.my/id/eprint/50659/1/_TechnicalPapers_CAFEi2012_56.pdf |
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| author | Liaghat, Shohreh Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Meon, Sariah Ehsani, Reza Md Nor Azam, Siti Hajar |
| author_facet | Liaghat, Shohreh Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Meon, Sariah Ehsani, Reza Md Nor Azam, Siti Hajar |
| author_sort | Liaghat, Shohreh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Basal stem rot (BSR) is a serious fungal disease in oil palm plantations which potentially could reduce the market share of palm oil for Malaysia. Due to the preventing great losses in production and reducing the use of chemicals, early detection of Ganoderma fungal infection is critical for management of this disease. At present study, we propose to apply a mid-infrared spectroscopy technique for detection of infected oil palm trees at three stages of infection. Leaf samples of healthy, mild, moderate and sever-infected trees were measured using Fourier transform infrared (FTIR) spectrometers system to obtain absorbance data from the range of 2.55-25.05 μm. Single bounce ATR accessory with and without dilution with potassium bromide (KBr) were used in this study. Savitsky-Golay method was used for smoothing. The selected principal component (PC) scores were used as input features in quadratic discriminant analysis (QDA) as a pattern recognition algorithm. The results indicated that QDA-based algorithm can distinguish between healthy and infected leaves at three stages of infection with high classification accuracies (>80%) when leaves demonstrated that the proposed technique has the potential for early detection of the Ganoderma disease. |
| first_indexed | 2025-11-15T10:25:02Z |
| format | Conference or Workshop Item |
| id | upm-50659 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:25:02Z |
| publishDate | 2012 |
| publisher | Faculty of Engineering, Universiti Putra Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-506592017-03-02T06:11:06Z http://psasir.upm.edu.my/id/eprint/50659/ FT-IR absorbance data for early detection of oil palm fungal disease infestation Liaghat, Shohreh Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Meon, Sariah Ehsani, Reza Md Nor Azam, Siti Hajar Basal stem rot (BSR) is a serious fungal disease in oil palm plantations which potentially could reduce the market share of palm oil for Malaysia. Due to the preventing great losses in production and reducing the use of chemicals, early detection of Ganoderma fungal infection is critical for management of this disease. At present study, we propose to apply a mid-infrared spectroscopy technique for detection of infected oil palm trees at three stages of infection. Leaf samples of healthy, mild, moderate and sever-infected trees were measured using Fourier transform infrared (FTIR) spectrometers system to obtain absorbance data from the range of 2.55-25.05 μm. Single bounce ATR accessory with and without dilution with potassium bromide (KBr) were used in this study. Savitsky-Golay method was used for smoothing. The selected principal component (PC) scores were used as input features in quadratic discriminant analysis (QDA) as a pattern recognition algorithm. The results indicated that QDA-based algorithm can distinguish between healthy and infected leaves at three stages of infection with high classification accuracies (>80%) when leaves demonstrated that the proposed technique has the potential for early detection of the Ganoderma disease. Faculty of Engineering, Universiti Putra Malaysia 2012 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/50659/1/_TechnicalPapers_CAFEi2012_56.pdf Liaghat, Shohreh and Mansor, Shattri and Mohd Shafri, Helmi Zulhaidi and Meon, Sariah and Ehsani, Reza and Md Nor Azam, Siti Hajar (2012) FT-IR absorbance data for early detection of oil palm fungal disease infestation. In: International Conference on Agricultural and Food Engineering for Life (Cafei2012), 26-28 Nov. 2012, Palm Garden Hotel, Putrajaya. (pp. 176-180). http://cafei.upm.edu.my/download.php?filename=/TechnicalPapers/CAFEi2012_56.pdf |
| spellingShingle | Liaghat, Shohreh Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Meon, Sariah Ehsani, Reza Md Nor Azam, Siti Hajar FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title | FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title_full | FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title_fullStr | FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title_full_unstemmed | FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title_short | FT-IR absorbance data for early detection of oil palm fungal disease infestation |
| title_sort | ft-ir absorbance data for early detection of oil palm fungal disease infestation |
| url | http://psasir.upm.edu.my/id/eprint/50659/ http://psasir.upm.edu.my/id/eprint/50659/ http://psasir.upm.edu.my/id/eprint/50659/1/_TechnicalPapers_CAFEi2012_56.pdf |