Tree detection and delineation has become one of the important factors that contribute to precision agriculture specifically in plantation industry and efficient forestry management. However, this requires tools and technology that would give reliable information and high accuracy data processing. Recent researches aimed at providing this goal by utilizing the advancement of available remote sensing technology and integrate various algorithms. This article reviews these researches with a focus on algorithms applied to remote-sensing imagery for the purpose of tree detection and delineation. It categorizes and evaluates those methods with the respective types of remote sensing imagery used in the area to evaluate the influence of these factors on the method. Finally, it summarizes the finding of the current algorithms, and suggests on the new development that can be expected in the future.

Malaysia is one of the largest palm oil producers in the world. To meet the production, its plantation needs to be monitored accordingly. Manual monitoring is no longer feasible due to energy, cost and time consumption. Remote sensing technology such as satellite images can be utilized for palm oil monitoring as they are cost effective, time and energy saving, and able to enhance the possibility of classifying the vegetation through spectral and texture analyses. Due to its impact towards Malaysia economy, an effective monitoring of palm oil plantation can be done by using satellite image to maintain the sustainability of palm oil in Malaysia. This project utilized the computation of Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) to determine the plant health. Relationship of NDVI and SAVI with respect to temperature and precipitation is studied based on the data obtained from Malaysian Meteorology Department. By using regression model technique, the relationship of the plant health based on NDVI analysis, nitrogen content based on SAVI analysis and temperature is found out to be highly correlated while plant health and precipitation is found out to be no or low correlated. The results can be utilized by the plantation operators, especially independent smallholders in monitoring the health status of the palm oil trees to ensure high productivity.

In this paper, we present 3D point cloud data segmentation from a Terrestrial Laser Scanner (TLS) using RANSAC and localization approaches. Our work is using the real world data acquired from outdoor and indoor scene of Tawau Bell Tower (also known as the Belfry), one of the heritage building located in Sabah. We adapt the methods to segment ground and nonground data for data reduction. Based on the non-ground information and geometrical constraint, the data managed to show the building structure. The aim is to use and handle a smaller set of data rather than using the entire point cloud data in processing the region of interest. This will also help to reduce processing time as point cloud data from a TLS is usually large in size. The ground plane was fitted with RANSAC algorithm restricted by distance to describe the geometrical of the plane. As a result, the point cloud data was reduced to the environment around Bell Tower and able to highlight the region of interest. Building segmentation is then retrieved from the reference centre location to form a bounding box and data were organized using kd-tree to denote the Bell Tower structure. The framework is tested for outdoor and indoor scan of the Bell Tower and able to segment the data appropriately.

The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.

Nowadays, interior scanning is very significant in mobile robot applications because it helps humans to monitor, study and investigate the environments, especially the challenging ones with clutter, hazardous, unexplored, dynamic, and others. A LIDAR can be utilised to perform the task; however, suitable mobile platform should be accompanying it to allow mobility while collecting data representing the environment. This paper shows the process of developing a suitable mobile platform for a low-cost laser rangefinder, RP LIDAR from SLAMTEC with its scanning performance. The mobile platform used in this project is an Arduino Uno R3 4WD mobile robot. L298 H-Bridge IC and HC-05 Bluetooth module is implemented on the robot to make the user able to control its movement through a mobile application. MIT app inventor is used to develop the mobile application. Through the application, user can control the mobile robot with the LIDAR to move around the selected indoor area to scan its environment. To show the scanning results, MATLAB will be used to plot the map. Analysis from the plotting show good mapping results and similar to the real environment, thus presenting its potential to be used in the above-mentioned situations.

This paper aims to show the process of Augmented Reality (AR) development representing existing building interior from a 3D sensor. These days, AR is becoming well known and widely used in various type of areas such as in gaming industry, medical training, and education. Due to its advantages, AR development for building interior is also necessity, specifically to assist people in understanding the scene more, in Architectural, Engineering, Construction / Facility Management (AEC/FM) applications as well as businesses like interior designing and household marketing. With respect to current situation of COVID-19 pandemic that has restricted travelling makes the usage of AR very beneficial and it could assist people to visualize the interior that needs to be visited. With the aid of suitable sensors like 3D sensor, the development of AR representing existing building interior which is usually full of clutter and occlusion could be eased.