000			04236nam a22003377a 4500
003			OSt
005			20250715172614.0
008			250714b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bC33 2025
100			_aCabrillas, Robin John T. _eauthor
245			_aMushkin: _bEnhancing mushroom yield production via long short-term memory (lstm) with monitoring using yolo v8 for image processing in an iot-based urban agriculture/ _cRobin John T. Cabrillas, Karl C. Cabulagan, Adrian C. Dela Cruz, Ayessa Denise S. Opanda, John Harold S. Ricafrente, and Maica V. Tameta.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_axvi, 217pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Engineering.-- _bBachelor of science in electronics engineering: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aMillions of people in the Philippines continue to struggle with food insecurity. Growing mushrooms has become a popular way to increase food production in constrained areas through urban agriculture. This paper presents MushKin, an Internet of Things (IoT)-based system that combines YOLO v8 with Long Short-Term Memory (LSTM) models for real- time monitoring and yield optimization in mushroom farming. Environmental sensors are used by the system to track variables like light intensity, substrate wetness, CO2 levels, temperature, and humidity. While LSTM algorithms estimated ideal growing conditions, the ESP32 microcontroller gathers data to enable autonomous climate management. YOLO v8 also makes it easier to identify issues and recognize growth stages, which improves cultivation efficiency. To provide accessibility and user-friendliness, a web-based platform enables farmers to remotely monitor and control the system. MushKin was tested and evaluated at Bigay Buhay Multipurpose Cooperative (BBMC) to compare it with conventional farming practices. Results show that MushKin significantly increases sustainability, decreases labor-intensive monitoring, and improves yield prediction accuracy for five different mushroom species: oyster, milky, reishi, chestnut, and shiitake. The MushKin method has been shown to enhance productivity, according to statistical research, including T-tests. The precision and productivity of the Mushkin system were shown to be significantly higher than those of conventional mushroom cultivating techniques. Reliable real-time detection and measurement were made possible by the model's 0.707 precision, 0.637 recall, and 0.673 [email protected], all of which were obtained using YOLOv8. When compared to hand measurements, size estimations of cap area, length, and diameter for five different species of mushrooms consistently had percentage errors below 7%, demonstrating the consistency and quality of the approach. Additionally, significant increases were found when comparing the yields of Mushkin and conventional farming methods. Cap size, stem length, and total harvested weight all showed statistically significant changes, favoring tradition supported by Mushkin. Extended harvesting times were also made possible by Mushkin-enabled systems, which raised the overall yield. T-tests confirmed these results, confirming the system's ability to improve quality and productivity. This study demonstrates how AI-powered IoT solutions can be used in urban agriculture to support sustainable food production. MushKin offers an efficient and effective method of mushroom farming by combining automation, predictive analytics, and smart monitoring, opening the door for more extensive precision agriculture applications.
650			_aAi-powered
650			_aLong short-term (lstm)
650			_aInternet of things (iot)
700			_aCabulagan, Karl C. _eauthor
700			_aDela Cruz, Adrian C. _eauthor
700			_aOpanda, Ayessa Denise S. _eauthor
700			_aRicafrente, John Harold S. _eauthor
700			_aRicafrente, John Harold S. _eauthor
942			_2lcc _cBTH COE _n0
999			_c30348 _d30348