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Author: search.filters.author.Khurnpoon, LampanSubject: search.filters.subject.Durian leaf

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    Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection
    (2024-01-05)
    Phanomsophon, Thitima
    ;
    Jaisue, Natthapon
    ;
    Worphet, Akarawhat
    ;
    Tawinteung, Nukoon
    ;
    Khurnpoon, Lampan
    Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500–3,600 cm<sup>−1</sup>. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r<sup>2</sup> = 0.852, SEP = 0.14%, RPD = 2.63 and bias = −0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r<sup>2</sup> = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.
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    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
    (2022-11-15)
    Phanomsophon, Thitima
    ;
    Jaisue, Natthapon
    ;
    Worphet, Akarawhat
    ;
    Tawinteung, Nukoon
    ;
    Shrestha, Bijendra
    For 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.
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    Classification of N, P, and K concentrations in durian (Durio zibethinus murray cv. mon thong) leaves using near-infrared spectroscopy
    (2022-08-24)
    Phanomsophon, Thitima
    ;
    Jaisue, Natthapon
    ;
    Tawinteung, Nukoon
    ;
    Khurnpoon, Lampan
    ;
    Sirisomboon, Panmanas
    A good durian fruit should be supported by sufficient nutrient concentrations. If farmers can quickly determine the nutrient concentration in leaves, they can control fertilisation. Near-infrared (NIR) spectroscopy is a rapid non-destructive test for estimating nutrient concentration. Measuring nutrient concentrations requires chemical analysis and is a time-consuming process. The aim of this paper is a preliminary test for classifying the N, P, and K concentration levels to determine the possibility of creating models in the future to help farmers obtain information on durian tree nutrient requirements quickly. The models reported in this paper were created by PLS-DA, which had higher accuracy than SVM and the SIMCA method. Fresh durian leaf samples were used, and the spectra provided greater accuracy than the dried ground leaf sample spectra. The classification models had an accuracy of 88.89, 81.25, and 86.36% for predicting N, P, and K, respectively.