Radar–drone system is potentially implemented as a method for collecting the soil water content of a large area. Vegetation that may covers the soil surface affects the detection results of soil water content using radar system in plantation areas. Vegetation will influence the propagation mechanism of radar waves, therefore, a method to overcome this effect is needed. The method to compensate the effect of vegetation based on a transmission line model is then proposed in this paper. The transmission line model is used as the concept for transforming the measured reflection coefficient under the vegetation effect to the ground reflection coefficient value. Experiments were carried out by taking a case studies on tea plantations. The tea plant becomes a vegetation layer which its effect needs to be considered on the detection of soil water content. An ultra-wideband radar system with a frequency range of 500 MHz - 3 GHz is proposed in this study. The radar is integrated to hexacopter drone for scanning the tea plantation areas. The radar-drone system flight with a constant elevation from ground level. The experimental results show that the proposed method is able to improve the detection results of soil water content using radar with an accuracy of 96%. The radar-drone performance in detecting soil water content is suitable for precision farming purposes.

In this research uses the method a Local Binary Pattern (LBP) as a method of the face recognition. LBP used because they have computing is its simple allow can get the result by the time relatively fast. LBP will take the facial feature extraction by dividing it into several small areas. This study attempts to mobile developed an application based on android operating system to recognize the face in real-time. The methodology that was used on the application this uses the method LBP with a camera in front of a device android. This research will test the accuracy of LBP method in recognizing faces with variations in the level of light, the distance between front camera device and variations of the angle face. In this study was obtained the level of accuracy LBP for face recognition with variations of light of 90% from test in the morning. To variations in distance of 90% from test in distance 25cm, 50cm and 125cm for the angle of the face of 90% from test 0⁰ until 30⁰.

Arowana is a kind of fish which requires routine and patience maintenance techniques, particularly in terms of feeding and conditioning the water in aquarium which is quite specific. Lack of proper maintenance may affect the physical growth of arowana. It has been built a system to monitor and maintain the arowana, called Automatic Arowana Raiser (AURORA). So that the fishes can be monitored and the aquarium circumstances-such as water temperature, water treatment, and control the feeding can be maintained easily. The applications based android then linked with the cloud service. Furthermore, cloud service forward the instructions that sent by the application to be executed by the device. It is expected with this system, will allow owners of arowana to fish-keeping their pet with a more efficient time.

Chemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated density distribution map of a designated area. Multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are deployed strategically within partitioned regions, determined through weighted Voronoi tessellation. This method integrates both the robots’ internal parameters and environmental factors. The distinct operational domains of UAVs and UGVs facilitate region decomposition by accounting for variations in CRN dispersion, obstacle representation, and environmental conditions. The resulting cross-partitioned regions are systematically merged to enhance robot distribution efficiency. Each robot autonomously measures within its allocated region, updates contamination data, and generates a dispersion map. The proposed strategy enables an adaptive robot distribution, eliminating uncontaminated grids and improving mapping accuracy. Compared to existing methods, including homogeneous schemes, our approach reduces data variance in CRN-contaminated regions while maintaining mapping efficiency.