000			02637nam a22002417a 4500
003			FT8784
005			20251128090940.0
008			251128b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT58 A23 2025
082			_a.
100	1		_aAbaga, Firne Ally C.; Caca Jr., Isagani S.; Dela Llana, John Kelvin M.
245			_aIoT-based small scale hydroponics system and lettuce disease and harvestability detection using yolov8
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
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505			_aABSTRACT: This study presents the development of an IoT-based hydroponics system integrated with artificial intelligence to automate the monitoring and analysis of lettuce health and growth status. Traditional methods of detecting plant diseases and determining harvest readiness rely heavily on manual inspection, which can be time-consuming, inconsistent, and prone to human error. To address these challenges, the researchers developed a smart system that combines real-time sensor monitoring with AI-powered image detection to improve accuracy and efficiency in crop management. The hardware setup includes an Arduino Uno connected to multiple sensors for measuring pH, temperature, humidity, and TDS levels, with an ESP8266 module handling wireless communication and relay control for managing pumps, lights, and essential environmental systems. The sensors achieved a margin of error ranging from 0.01 to 0.01, ensuring precise and reliable environmental monitoring. A Django-based web application was developed to remotely upload images of lettuce and monitor sensor data in real-time. The AI component utilizes YOLOv8 deep learning models to analyze images and accurately detect lettuce such as downy mildew, septoria blight, and viral infections, as well as determine harvest readiness. The models achieved a mean Average Precision (mPA@50) ranging from 0.6 to 0.9 across different classes and test conditions. The system was tested in a controlled hydroponic environment and demonstrated reliable performance in identifying plant health issues and optimizing harvesting decisions. By integrating machine learning with IoT, the project significantly improved decision-making, reduced manual labor, and highlighted the potential of intelligent automation in enhancing productivity and sustainability in modern agricultural systems.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
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