Maramara, Sophia A.; Pisos, Jayson G.; Ras, Francis Jericho F
Plantery: An IoT-based automated plant watering system and pest detection using convolutional neural network (CNN) - Capstone Project: (Bachelor of Science in Information Technology) _ Pamantasan ng Lungsod ng Maynila, 2025
ABSTRACT: 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.
academic writing
T59.5 M37 2025
Plantery: An IoT-based automated plant watering system and pest detection using convolutional neural network (CNN) - Capstone Project: (Bachelor of Science in Information Technology) _ Pamantasan ng Lungsod ng Maynila, 2025
ABSTRACT: 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.
academic writing
T59.5 M37 2025