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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Real Time Anomaly Detection Techniques Using PySpark Frame Work
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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
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Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
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Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
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ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
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Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 3 | Issue - 2 | june 2021
Published
22 June, 2021
Herbal plants are crucial to human existence for medical reasons, and they can also provide free oxygen to the environment. Many herbal plants are rich in therapeutic goods and also it includes the active elements that will benefit future generations. Many valuable plant species are being extinguished and destroyed as a result of factors such as global warming, population growth, occupational secrecy, a lack of government support for research, and a lack of knowledge about therapeutic plants. Due to the lag of dimensional factors such as length and width, many existing algorithms fail to recognize herbal leaf in all seasons with the maximum accuracy. Henceforth, the proposed algorithm focuses on the incomplete problems in the datasets in order to improve the detection rate for herbal leaf identification. The inclusions of dimension factors in the datasets are performing good results in the image segmentation process. The obtained result has been validated with a machine learning classifier when combined with ex-or gate operation is called deep knowledge-based identification. This two-stage authentication (TSA) procedure is improving the recognition rate required for the detection of herbal leaf. This fusion of image segmentation with machine learning is providing good robustness for the proposed architecture. Besides, intelligent selection of image segmentation techniques to segment the leaf from the image is improving the detection accuracy. This procedure is addressing and answering the drawbacks associated with the detection of the herbal leaf by using many Machine Learning (ML) approaches. Also, it improves the rate of detection and minimizes the classification error. From the results, it is evident that the proposed method has obtained better accuracy and other performance measures.
KeywordsHerbal Plant Identification Edge Detection Machine Learning
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