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    Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images
    (2022-07-01)
    Chungcharoen, Thatchapol
    ;
    Donis-Gonzalez, Irwin
    ;
    Phetpan, Kittisak
    ;
    Udompetaikul, Vasu
    ;
    Sirisomboon, Panmanas
    This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r<sup>2</sup>), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r<sup>2</sup>, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r<sup>2</sup> and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r<sup>2</sup>, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves.
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    Technical efficiency of oil palm production under a large agricultural plot scheme in Thailand
    (2018-01-01)
    Juyjaeng, Cha On
    ;
    Suwanmaneepong, Suneeporn
    ;
    Mankeb, Panya
    Background and Objective: Thai government launched a Large Agricultural Plot Scheme (LAPS) in 2005 in order to enhance the effectiveness of the extension programme. The objective of this research was to compare the technical efficiency (TE) of oil palm production and factors influencing the TE of oil palm production between member and non-member farmers under the LAPS in Bang Saphan Noi district, Prachuap Khiri Khan Province, Thailand. Methodology: The data were collected from January-June, 2017 from 57 LAPS member farmers and 63 non-LAPS member farmers. This paper estimated technical inefficiency by using a stochastic production frontier model and Tobit regression to investigate the factors influencing the TE. Results: The results revealed that the TE of oil palm production of the LAPS member farmers was ranked 12-99%, whereas that of the non-LAPS member farmers was ranked 14-99%. The TE mean of LAPS member farmers was 0.63, while the TE mean of non-LAPS member farmers was 0.52. The years of experience on oil palm plantations and age were crucial factors that contributed to the TE of LAPS member farmers and TE of non-LAPS member farmers, respectively. Conclusion: The TE mean of LAPS member farmers was higher than that of non-LAPS member farmers knowledge and experience sharing between old and young generations who are eager to work in oil palm production should also be a concern.