Malaysians generate 15,000 tons of food waste per day and dispose of it in the landfill, contributing to greenhouse gas emissions. As a solution for the stated problem, this research aims to produce an excellent quality bokashi compost from household organic waste using a smart composting bin. The bokashi composting method is conducted, whereby banana peels are composted with three types of bokashi brans prepared using 12, 22, and 32 mL of EM-1 mother cultured. During the 14 days composting process, the smart composting bin collected the temperature, air humidity, and moisture content produced by the bokashi-composting process. With the ATmega328 microcontroller, these data were uploaded and synchronized to Google Sheet via WIFI. After the bokashi-composting process was completed, three of each bokashi compost and a control sample were buried in separate black soil for three weeks to determine each compost’s effectiveness. NPK values and the C/N ratio were analyzed on the soil compost. From the research, 12 mL of EM-1 shows the most effective ratio to the bokashi composting, as it resulted in a faster decomposition rate and has an optimum C/N ratio. Bokashi composting can help to reduce household food wastes. An optimum amount of the EM-1 used during the bokashi-composting process will produce good quality soil without contributing to environmental issues.
This paper presents the use of the received signal strength indicator (RSSI) from the RF signal to estimate the distance from a point where the signal is transmitted to the point where the signal is received. This can be a challenge as in the paddy field, the watery and dry conditions, as well as the height of the paddy plant can affect signal transmission during this estimation process. Two low-cost ground beacons, Beacon1 and Beacon2 (The coordinator), are used and placed in a known location with a fixed distance across the paddy field, which becomes the reference point during the distance estimation for the unmanned aerial vehicle (UAV). These signals are analyzed by using the non-right-angle trigonometry computation, to estimate the distance between the transmitter and the receiver. The estimated distance is compared with the measured value to determine the efficiency of this approach. The calibration trendlines of these beacons in the open, watery and dry paddy fields are discussed and presented. It is found that the dry paddy field gives less RSSI mean error and proved that humidity can contribute to the distance estimation error.
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