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Traditional methods like counting and measuring the height of the trees manually are time-consuming and also costly, which can be a challenge in the oil palm industry. Therefore, having an automatic system to count trees and measure their height in an oil palm plantation is useful in production planning and decision-making. Using Unmanned Aerial Vehicles (UAVs) to count trees and measure their height has become more popular due to its advantages in ecosystem and forestry industries. In this study, some image processing techniques are used to process the dataset captured by UAV in order to extract the height and number of oil palm trees from the dataset. These datasets is captured using a MicaSense RedEdge Multispectral Sensor that is attached to a UAV that flying 80m above sea level. The processed datasets include the Canopy Height Model (CHM), Digital Surface Model (DSM), and Digital Terrain Model (DTM). This study shows relatively good accuracy between the predicted and prepared CHM where R-squared (R2)=0.74, Mean Absolute Error(MAE)=0.28, and Root Mean Square Error(RMSE)=0.38. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), with sensors that can detect spectrums are useful tools for spotting diseases early in crops. This research zooms in on identifying Ganoderma disease in oil palm farms in Melaka and Sabah using UAV images. This research analyzed RGB pictures taken by the UAV mounted camera and multispectral data boosting their precision with georeferencing techniques. By employing the Normalized Green-Red Difference Index (NGRDI) and Normalized Difference Vegetation Index (NDVI) for image examination we effectively outlined the boundaries of oil palm trees. Future research should explore deep learning algorithms and further exploit NDVI to enhance disease detection. These developments are vital for improving strategies to detect and manage Ganoderma disease in oil palm plantations. [ABSTRACT FROM AUTHOR]

The transaction data is underutilized after the e-commerce activity has successfully ended. This useful transactional data should be analyzed to discover some useful insights into the company to increase business performance. Therefore, the primary objective of this study is to develop an enhanced Recency, Frequency, Monetary (RFM) model for analyzing online purchasers using transactional data. The model has an additional variable which is customer satisfaction (CSAT). Based on the findings of this study, CSAT is found to have a relatively important influence on online purchasing patterns. [ABSTRACT FROM AUTHOR]