Plant Species Identification Using a Custom CNN Model and Geometric Morphometrics – A Comparative Analysis
Keywords:
Plant leaf identification, Convolutional Neural Networks, Morphometric Features, Deep Learning, Image Classification, Agricultural AutomationAbstract
The purpose of this study is to assess how well CNNs and different classifiers perform when it comes to identifying plant leaves based on their morphometric characteristics. Two methods have been used for this. 1) Using our custom named PLI-CNN(Plant Leaf Identifier-CNN) model 2) utilizing conventional classifiers to extract features from leaf images for classification. To identify distinct patterns, a dataset of leaf images from 10 different plant types is used to train our PLI-CNN model. Multiple classifiers are utilized to extract and classify morphometric features simultaneously. According to the results, classical classifiers can also achieve high accuracy using quality data, despite CNNs' superiority at feature learning. This suggests that both approaches can benefit from continued study into plant species identification and automated botanical studies.
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