000			01907nam a22002417a 4500
003			FT8789
005			20251112131936.0
008			251112b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT59.5 M37 2025
082			_a.
100	1		_aMaramara, Sophia A.; Pisos, Jayson G.; Ras, Francis Jericho F
245			_aPlantery: An IoT-based automated plant watering system and pest detection using convolutional neural network (CNN)
264		1	_a. _b. _cc2025
300			_bCapstone Project: (Bachelor of Science in Information Technology) _ Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: The study titled PLANTery: An IoT-Based Automated Plant Watering System and Pest Detection using Convolutional Neural Network aimed to address the struggles faced by busy individuals who want to consistently care for plants, especially in terms of timely watering and pest management. PLANTery integrates automated watering and pest detection functionalities, ensuring optimal care with minimal user intervention. The system uses IoT components such as soil moisture, temperature, and humidity sensors to automatically trigger irrigation when needed. A Convolutional Neural Network (CNN) processes real-time images to detect pests and activates a spray mechanism while notifying users via a mobile app. Results show that the system accurately maintains soil moisture, detects pets with high confidence, and responds promptly through automated controls. Users confirmed the system’s reliability, case of use, and efficiency through successful real-world application. Overall, PLANTery effectively meets its objectives, offering a robust, smart solution for modern, low-maintenance plant care.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37082 _d37082