Items where Author is "Noor Azmi, Aiman Nabilah"

Bejo, Siti Khairunniza and Shahibullah, Muhamad Syahir and Noor Azmi, Aiman Nabilah and Jahari, Mahirah (2021) Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine. Applied Sciences, 11 (22). art. no. 10878. pp. 1-17. ISSN 2076-3417

Abdul Aziz, Mohd Hamim and Bejo, Siti Khairunniza and Wayayok, Aimrun and Hashim, Fazirulhisyam and Kondo, Naoshi and Noor Azmi, Aiman Nabilah (2021) Temporal changes analysis of soil properties associated with Ganoderma boninense Pat. infection in oil palm seedlings in a controlled environment. Agronomy, 11 (11). art. no. 2279. pp. 1-14. ISSN 2073-4395

Noor Azmi, Aiman Nabilah (2021) Early detection of Ganoderma boninense in oil palm seedlings using hyperspectral images and machine learning classification techniques. Masters thesis, Universiti Putra Malaysia.

Noor Azmi, Aiman Nabilah and Jahari, Mahirah and Yule, Ian J. and Bejo, Siti Khairani and Muharam, Farrah Melissa (2021) Differences between healthy and Ganoderma boninense infected oil palm seedlings using spectral reflectance of young leaf data. Basrah Journal of Agricultural Sciences, 34 (spec.1). 171 - 179. ISSN 2520-0860; ESSN: 1814-5868

Noor Azmi, Aiman Nabilah and Bejo, Siti Khairunniza and Jahari, Mahirah and Muharram, Farrah Melissa and Yule, Ian J. (2021) Identification of a suitable machine learning model for detection of asymptomatic Ganoderma boninense infection in oil palm seedlings using hyperspectral data. Applied Sciences, 11 (24). art. no. 11798. pp. 1-17. ISSN 2076-3417

Noor Azmi, Aiman Nabilah and Bejo, Siti Khairunniza and Jahari, Mahirah and Muharam, Farrah Melissa and Yule, Ian J. and Husin, Nur Azuan (2020) Early detection of Ganoderma boninense in oil palm seedlings using support vector machines. Remote Sensing, 12 (23). art. no. 3920. pp. 1-21. ISSN 2072-4292

Husin, Nur Azuan and Bejo, Siti Khairunniza and Noor Azmi, Aiman Nabilah and Ahmad, Desa (2018) Comparison between linear and quadratic models for Ganoderma classification. In: 39th Asian Conference on Remote Sensing (ACRS 2018), 15-19 Oct. 2018, Renaissance Kuala Lumpur Hotel, Malaysia. (pp. 771-780).