Implementation of Deep Learning for Identification of Rhizome Plants Using Convolutional Neural Network Method
Abstract
Rhizome plants are spices widely used by Indonesian people as cooking ingredients or traditional medicine. These plants have
similar appearances, making them difficult to distinguish for some people. Errors in identifying rhizome plants can lead to
poisoning, allergies, or unwanted side effects. To simplify identifying these plants, a system is needed to detect and differentiate
types of rhizome plants, which can be achieved using Convolutional Neural Networks (CNN) with the YOLO algorithm. CNN is
a Machine Learning technique capable of identifying objects based on their visual features, enabling efficient differentiation of
rhizome plants. The image dataset used is divided into six classes, with a total of 700 images. Model testing produced results
with a precision of 98%, recall of 99%, and mAP50-95 of 96%. Future research is expected to increase dataset variety to avoid
overfitting.
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