Computer vision : (Record no. 100684)

MARC details
000 -LEADER
fixed length control field 02359nam a22002417a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20261110105300.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 261110s2026 |||||||| |||| 00| 0 eng d
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number T ENG
Item number A28 2026
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Aburquez, John Cody S.
245 10 - TITLE STATEMENT
Title Computer vision :
Remainder of title based drone pineapple maturity detection : fuzzy logic and yolo approach /
Statement of responsibility, etc. John Cody S. Aburquez, Sherwin S. Aratea Jr., Mark Francis A. Bautista, and Carl Darwin M. Gundran.
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture EAC Cavite :
Name of producer, publisher, distributor, manufacturer School of Engineering,
Date of production, publication, distribution, manufacture, or copyright notice School of Engineering.
300 ## - PHYSICAL DESCRIPTION
Extent x, 131 pages ;
Dimensions 28 cm.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
500 ## - GENERAL NOTE
General note Experimental-developmental research<br/>Agile method
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references
520 ## - SUMMARY, ETC.
Summary, etc. The manual assessment of pineapple ripeness remains a time-consuming and inconsistent practice among local farmers, often resulting in delayed harvesting and post-harvest losses. This study developed a drone-based system capable of detecting pineapples and classifying their maturity to support precision agriculture and reduce manual labor in harvest decisions. A DJI Mini 3 drone captured aerial footage of pineapple fields, which was processed using a YOLO object detection model to locate pineapples and a Mamdani-type fuzzy logic system to classify ripeness as unripe, ripe, or overripe based on peel color, namely green, yellow, and brown. The YOLO model was trained on 4,622 raw and 7,766 augmented images, attaining a precision of 75%, recall of 74%, mAP50 of 76%, and F1 score of 75%. In laboratory testing, the system correctly classified 12 of 15 samples for an overall accuracy of 80%, with an average confidence score of 85.93%. The fuzzy logic component performed best for the Ripe category, with precision, recall, and F1-score of 1.00, while the Unripe category recorded a lower recall of 0.40 due to inconsistent lighting. Reliability measures, namely Cohen's Kappa (k = 0.700), Fleiss' Kappa (к = 0.691), and Krippendorff's Alpha (a=0.701), indicated substantial agreement with ground truth observations, supporting the system's potential to improve harvesting efficiency in the local pineapple industry.
700 1# - ADDED ENTRY--PERSONAL NAME
Relator code author
Personal name Aratea Jr., Sherwin S.
700 1# - ADDED ENTRY--PERSONAL NAME
Relator code author
Personal name Bautista, Mark Francis A.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type THESIS
Call number prefix EAC Cavite-Thesis Section
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Library of Congress Classification     EAC Library Cavite EAC Library Cavite EAC Cavite-Thesis Section 11/10/2026   T ENG A28 2026 ML-TH2841 11/10/2026 11/10/2026 THESIS