<![CDATA[Image processing is a technique to translate an image into digital form and execute some operations on it to obtain an improved image or extract some useful information from it. FloraFolium aimed to address the challenges in plant identification, especially in the Philippines, where many plant species are not well-studied or properly recorded. Many people struggle to tell which plants are edible, medicinal, or toxic because of limited access to official guides and reliable information. To solve this problem, the FloraFolium project created a mobile application that uses Convolutional Neural Networks (CNNs) to identify plant leaves and classify them into three categories: edible, medicinal, or toxic. The system was tested and evaluated based on ISO 25010 software quality standards. The results showed high ratings for functionality, usability, and efficiency, making the app reliable for everyday use. While the app performed well, some areas, like security and reliability in unusual conditions, need improvement. The study also found that the image quality greatly affects the system's accuracy. A balanced dataset of 15,000 images was divided into 80% for training and 20% for testing/validation. The model achieved a test accuracy of 99% and an overall validation accuracy of 98.8%, with the best weights saved at epoch 20 during the 30-epoch training period. The FloraFolium app is a helpful tool for outdoor enthusiasts, gardeners, farmers, and anyone interested in learning more about plants. It can also help preserve traditional knowledge about medicinal plants]]>
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