Disease detection among the napa and cannonball cabbage using cnn / Kyle L. Magistrado, Nerielyn T. Pablo, and Sherah May P. Serrano

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Dissertation note: Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2023. Abstract: 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
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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

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