Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling

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dc.contributor.authorPhanomsophon, Thitima
dc.contributor.authorJaisue, Natthapon
dc.contributor.authorWorphet, Akarawhat
dc.contributor.authorTawinteung, Nukoon
dc.contributor.authorShrestha, Bijendra
dc.contributor.authorPosom, Jetsada
dc.contributor.authorKhurnpoon, Lampan
dc.contributor.authorSirisomboon, Panmanas
dc.date.accessioned2026-08-06T10:38:10Z
dc.date.available2026-08-06T10:38:10Z
dc.date.issued2022-11-15
dc.description.abstractFor durian growth to produce high-quality fruit, plants should receive sufficient nutrients. Currently, farmers apply various fertilisers to produce a large quantity and quality of durian fruit, irrespective of the actual nutrients that the plant requires. Accordingly, the production cost is high and non-renewable resources. Therefore, this study focused on rapid classification primary macronutrient levels in durian (Durio zibethinus Murray CV. Mon Thong) leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and investigated the effect of imbalanced data on efficient classification models. Contents of N, P, and K in durian leaves were measured via NIR with the wavelength range of 800–2,500 nm. Classification models were developed using partial least squares, k-nearest neighbour, and artificial neural networks (ANNs) with imbalanced and balanced data. The imbalanced data were balanced using a synthetic minority oversampling technique (SMOTE). In this study, the model regarding the fresh leaf sample performed better than that for the dried ground leaf sample. Moreover, the ANN was the best algorithm, exhibiting validation accuracies of classified levels corresponding to N = 0.99 and P = 0.97 when the data were analysed with SMOTE and K = 1.00 from the original balanced data. The imbalanced data affected biased classification when the models could increase the classification accuracy by applying balanced data for modelling.
dc.identifier.citationMeasurement Journal of the International Measurement Confederation, 203, 2022
dc.identifier.doi10.1016/j.measurement.2022.111975
dc.identifier.issn02632241
dc.identifier.other2-s2.0-85139819866
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13533
dc.sourceMeasurement Journal of the International Measurement Confederation
dc.subjectClassification
dc.subjectDurian leaf
dc.subjectImbalanced data
dc.subjectNIR spectroscopy
dc.subjectPrimary macronutrients
dc.titleRapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling
dc.typeArticle

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