000			03209nam a22003377a 4500
003			OSt
005			20250707105411.0
008			250707b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bC33 2024
100			_aCabacang, Laurence C. _eauthor
245			_aDevelopment of aquapulse: _ban iot-based with data analytics for aquaponic farms/ _cLaurence C. Cabacang, Ericka Lhaine C. Donceras, Trisha Mae D. Figueroa, Jonathan C. Lopez, Kristine Joyce V. Orfrecio, and Jerwin M. Torreña.--
260			_aManila: _bTechnological University of the Philippines, _c2024.
300			_axii, 158pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Industrial Technology.-- _bBachelor of engineering technology major in computer engineering technology: _cTechnological University of the Philippines, _d2024.
504			_aIncludes bibliographic references and index.
520			_aThe study focused on the integration of IoT (Internet of Things) technology with data analytics to improve aquaponic farming through AQUAPULSE, an IoT-based system for real-time monitoring of the pH, ammonia, dissolved oxygen, light, temperature, and water level. This system aimed to optimize conditions for both plants and fish, improving resource utilization, reducing waste, and minimizing environmental impact. It was implemented in an aquaponic farm, where data from sensors were collected and analyzed to evaluate its performance. Testing focused on crop yield, fish health, and water quality stability, with hypotheses set to assess system efficiency. The study also included the evaluation of social acceptance through surveys and informal interviews. The results showed a 15% increase in crop yield and a 20% improvement in fish health compared to traditional aquaponic systems. Water quality remained stable, and nutrient usage was optimized. The system was evaluated using the ISO 25010:2023 framework, demonstrating high functionality, usability, compatibility, and effectiveness. Additionally, the hardware and software successfully underwent functionality and reliability testing, ensuring its reliability when used in practice. The ratio of accuracy was 88.33%, confirming the reliability of the system in the measurement of the water quality parameters. By integrating real-time monitoring and data analytics, the system successfully addressed the limitations of existing aquaponic systems, providing actionable insights for farmers. AQUAPULSE proved to be a reliable, scalable solution for modern aquaponic management, supporting sustainable agriculture and urban food production, and contributing to the advancement of smart farming technologies, which are crucial for future food security.
650			_aIoT
650			_aData analytics
650			_aAquaponic farming
700			_aDonceras, Ericka Lhaine C. _eauthor
700			_a Figueroa, Trisha Mae D. _eauthor
700			_a Lopez, Jonathan C. _eauthor
700			_aOrfrecio, Kristine Joyce V. _eauthor
700			_aTorreña, Jerwin M. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c30162 _d30162