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- W4313462056 abstract "The digital era is transforming day by day and therefore extensive usage of the internet of things embedded with sensors, data storage, and machine learning is not only limited to computer science or manufacturing industries but has also paved its direction toward the botanical world. Plants could have many species which differ from each other with respect to their features at a very minute scale. Thus, botanists have always been in search of a tool that could help them in the classification of plants into appropriate species so that their time could be saved significantly. This paper is inclined to assist botanists in facilitating the categorization of unseen species of a plant into appropriate classes. This paper presents a complete framework utilizing machine learning-based algorithms for the classification of plants into the most appropriate class. The major contribution of the framework is its ability to extend to every breed of plant with as many species as possible. The model is subjected to the process of feature extraction using the Pearson correlation and Information Gain method. The standard performance comparison is drawn between classifiers; Support Vector Machine, Multinomial Naive Bayes, Extreme Gradient Boosting, Decision Tree, Random Forest, and K- nearest neighbor." @default.
- W4313462056 created "2023-01-06" @default.
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- W4313462056 date "2023-01-04" @default.
- W4313462056 modified "2023-09-29" @default.
- W4313462056 title "Machine Learning Framework for Recognition and Classification of Plant Species" @default.
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- W4313462056 doi "https://doi.org/10.1145/3571306.3571444" @default.
- W4313462056 hasPublicationYear "2023" @default.
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