| 000 -LEADER |
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02488nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8782 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251111093653.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251111b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T9 G83 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Guatno, Cris Paul V.; Obillo, Dean Emmanuel A.; Taguinod, Jovan E. |
| 245 ## - TITLE STATEMENT |
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| Title |
Harnessing AI for agriculture utilizing color-condition camera sensors and thermal imaging drones for crop color-condition detection and predictive yield analysis with inventory management system |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| 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 |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: Technological Gaps and Challenges for Agriculture in the Philippines Such advancements may be relevant to other countries as well but we should also consider that there is a greater potential of technology adoption with these purposes than ever before, especially those small-scale farmers from local areas who have less accessibility on technological trendsetting. In this study, the researchers attempt to solve these problems using Artificial Intelligence (AI) in order to improve crop monitoring and predictive yield amidst the climate change. Color-condition using Convolutional Neural Networks (CNN) with 76.97% accuracy and predictive analysis by Artificial Neural Network (ANN). This optimizes the timing of planting and harvest, depending on the combination of this algorithm. This study aims to capture the crops maturity through color-condition detection and gather data for yield analysis by capturing the thermal temperature of the soil. The AI can identify crop health and maturity issues, such as nutrient deficiencies. It will also enhance crop yield predictions by inputting information such as soil types, temperature, fertilizers used, and weather conditions. Additionally, this study implements a partial website system to serve as an interface for the images captured by the camera attached to the drone. This website also showcases the goals of the proposed study. Furthermore, it includes a crop stock and inventory management feature to reduce paper usage and promote an eco-friendly environment. Through this innovation, local farmers can adapt to new technologie |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
Thesis/Dissertation |