Soil moisture volume is the greatest attribute of soil. Irrigated farms rely on controlling the two fundamental raw materials; water and soil. Putting excessive water enlarges the pumping costs, decreasing the water effects to the soil, and cause contamination or pollutant. The study aims to develop an irrigation water management system that controls the volume and frequency of irrigation water applied to the soil and to use low-cost sensor device that measures the soil moisture level accurately like the high price sensor in the market. The FC-28 soil moisture sensor was also validated conducting (60) sixty trials with different soil and the readings are the same for soil moisture measurement devices in the market. Based on the testing, the whole system resulted in a 100% success rate in system and functionality testing. This study proves that the automatic irrigation system controlled by soil moisture sensor is efficiently and accurately.
Many kinds of research focused on the flood detection and monitoring, flood management, flood risk management and flood forecasting in urban areas, wherein a large number of populations lies chaos in mobility is high. Owing to natural disasters, flooding in these regions can lead to an increase in mortality rates. This project is primarily focused on the detection of a flood by installing a flood detector device with a camera beside the bridge column. The camera is facing the three lines with different colors. If one of the colors was tempered by the river water, the device will send an alarm to the community that the water level in the river is high. This aims to alert the community and the authorities to be aware and be ready for the approaching flood. Flood-Level Detection and Alert System proved 87.1%, 73.6%, and 95.69% testing accuracy of Green, Blue, and Red respectively. Overall, the accuracy of the whole system produced 85.46%.
Fire detection systems are implemented and intended to detect fires early so it can help the people on a building or house for safe evacuation and immediately notify the firemen. After the firemen put out the fire, that will be the time that they can conduct an investigation in determining the source or cause of fire which they often experience some difficulties. Therefore, this study proposed an algorithm for identification of combustible and fluid fire with hybrid feature extraction techniques. The algorithm use RGB model, applying HSV conversion and Canny edge detection for the growth of fire. Then combine the results of HSV and Canny edge detection and used image segmentation of color space for combustible and fluid fire. The algorithm got an accuracy of 94% for 50 fire images demonstrated usefulness and effectiveness. .
Today, indoor gardening provides a stable food supply in end consumers, restaurants, and retailers. Extreme heat affects stress to the plants that can lead to exhaustion. Research says that plants are said to be on optimal condition if the temperature only ranges between 15-34 degrees Celsius. Anything higher than the said temperature will cause plants to suffer, deteriorate, and also affect the plants' productivity. The researchers successfully developed an automatic mist machine that is capable of cooling a surrounding area that can control the rise of temperature in protecting and maintaining the optimal temperature required for indoor plants in a garden with a Rule-Based method. The results of the two trials were all the same when it comes to temperature and humidity over time. The rise of temperature was the decrease in humidity and vice versa, making it inversely proportional to each other. However, it resulted that the higher the height of placement of the prototype, the longer it takes to cool down the surrounding area. The study shows that the testing of functionality and validation of the system resulted in a 100% success rate. The study verifies and proves that the system is effective and efficient in the issue of the temperature control system.
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