Deep Feature Extraction and Weight Updated Tuned Random Forest for Piper Plant Species Recognition

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A. Pravin
C. Deepa


Recently, identifying plant species has become a significant research area as it is vital for securing biodiversity. Plants also possess various medicinal applications. Hence, predicting different species of plants is of utmost significance. However, determining plant species through conventional ways is a time-consuming process. That happens due to huge and distinct botanical terms. With the recent evolution of AI (Artificial Intelligence) based algorithms, researchers have undertaken various attempts to predict plant species. However, most studies averted the consideration of piper plant species which holds huge medicinal benefits. Existing research also failed to predict the plant species due to inefficient feature extraction accurately. Considering such a pitfall, this study proposes Deep CNN (Deep Convolutional Neural Network) and Inception V3 to extract features to perform all plant classification. In addition, the study proposes Deep CNN and VGG16 (Visual Geometry Group16) to extract suitable features for performing piper plant classification. Following this, the study considers PCA (Principle Component Analysis) for feature fusion as it can reduce noise in data and select relevant features for affording independent and uncorrelated data features. Finally, the study proposes WUT-RF (Weight Updated Tuned Random Forest) to classify piper and all plant species. In this process, hyperparameters of RF are tuned with convolutional likelihood weight to attain a high prediction rate. Optimal hyperparameter selection and tuning assist in improvising the performance of the proposed classifier. Performance analysis of this system about performance metrics exposes its effectiveness in plant species detection.


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How to Cite
Pravin, A., & Deepa, C. (2023). Deep Feature Extraction and Weight Updated Tuned Random Forest for Piper Plant Species Recognition. Advances in Systems Science and Applications, 23(2), 128-151.