02217nam a2200301 i 4500 CSPC 20250814154043.0 250814s2023 ph a b 000 0 eng d CSPC eng CSPC rda UTH BSCS M272d 2023 Magistrado, Kyle L. author. Disease detection among the napa and cannonball cabbage using cnn / Kyle L. Magistrado, Nerielyn T. Pablo, and Sherah May P. Serrano 150 leaves. rdacontent text rdamedia unmediated rdacarrier volume Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2023. Includes bibliographic references. Achieving sustained agricultural yield and global food security in agriculture required effective plant disease identification. This study focused on detecting common illnesses that affect cabbage plants, an essential green vegetable in the Philippines. Nevertheless, the increasing occurrences of plant diseases globally pose a significant threat to food secu-rity. CNN models were used to identify four common diseases that affect two commonly grown cabbage varieties in the Philippines: Napa (Chinese Pechay) and Cannonball cab-bage. This paper used deep CNN architectures, specifically ResNet-34 and VGG16, to distinguish between diseased and healthy leaves. The effectiveness of both algorithms in precisely recognizing and categorizing impacted cabbage leaves was examined. The ResNet-34 achieved a 97.03% model accuracy while the VGG16 achieved an accuracy of 97.86%. These models had the potential to transform plant disease detection by providing accurate and efficient disease identification, protecting crop yields and global food security in the face of changing agricultural problems Pablo, Nerielyn T. author. Serrano, Sherah May P. author. Fortuno, Kaela Marie N. adviser. Oñate, Joseph Jessie S. chair, panelist. Onesa, Rosel O. panelist. Pandes, Tiffany Lyn O. panelist. ddc 0 UM M272d BSCS UTH 2023 0 0 ddc 0 0 CSPC CSPC UTH 2025-08-14 0 UTH BSCS M272d 2023 006700UM 2025-08-14 15:41:05 1-1 2025-08-14 UM 30032 30032