| dc.description.abstract | Adequate nutrition is one of the important factors in its contribution to oil palm
productivity. Usually, Usually, fertilization is needed to obtain sufficient nutrients
based on the analysis of soil and leaf nutrients. Measuring leaf nutrient content
conventionally lacks flexibility, is impractical, is labour-intensive, and takes time
and is expensive, so remote sensing can be an alternative to addressing this
problem. The research uses an Unmanned Aerial Vehicle (UAV) equipped with
multispectral sensors that have the advantages of high spatial resolution, low cost,
easier operating systems, can fly low, and can be operated at any time The
independent variable used reflection value extraction for each band and 10
vegetation index transformations obtained from 3 multispectral camera bands (red,
green, and near-infrared), while the dependent variable used analysis of laboratory
leaf samples. The data is used to determine the best predictive model, independent
variable, acquisition time, and leaf nutrient mapping in oil palm plantations. The
best prediction model of nutrients N, P, K, Ca, and Mg use polynomial regression
of double order 4 with R2
values in the succession of 0.986; 0.975; 0.981; 0,970;
and 0.968, as well as Adjusted (Adj) R2
values successively 0.861; 0.761; 0,812;
0,710; and 0.690. The NDVI and GNDVI vegetation index combined with the single
band NIR become the best predictor variables in the predictive model. The best time
for image data acquisition is daytime with a MAPE value ratio of 13.03%, the
lowest than morning and afternoon acquisitions for all predictive models. The
output prediction is the nutritional value of the leaves of each nutrient for each
plant, presented spatially to provide accuracy and ease of use. | en_US |
