| 000 -LEADER |
|---|
| fixed length control field |
01907nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
FT8789 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251112131936.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
251112b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
T59.5 M37 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Maramara, Sophia A.; Pisos, Jayson G.; Ras, Francis Jericho F |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Plantery: An IoT-based automated plant watering system and pest detection using convolutional neural network (CNN) |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) _ Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
|---|
| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |