Deep learning based identification of aquatic and semi aquatic plants

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AbstractBotanists use morphological features of leaves for aquatic and semi‐aquatic plants identification. In deep learning, the convolution process is efficacious to label images. Existing studies showed that, in deep learning‐based leaf identification, direct image pixel values are used. Leaf images have similar sizes and almost equal pixel values. So, the pixel‐based leaf naming procedure makes uncertainty in the feature results. The direct evaluation of image pixels for leaf image identification is not an effective method, because it is leading to a forgetting problem in continuous learning. Using the pre‐processed databases, researchers are achieving more than 99% accurate results using deep learning. However, the same model fails to reproduce the same result in databases. In this study, morphological features of aquatic and semi‐aquatic plant leaves are converted into digital descriptors to avoid uncertainty in the feature results. In this digital descriptor‐based deep learning method, morphological features of aquatic and semi‐aquatic plant leaves are used. This is equivalent to Botanists' morphological features‐based leaf identification technology. Morphological feature extraction and digital descriptor generation helped this model to achieve 95% accuracy. In this model, leaf morphological features are used for training, so this help to understand the leaf properties of other leaf databases.