ABSTRACT
In this COVID-19 pandemic era, where people are losing their lives, there are several species and plants available on our Mother Earth that are beneficial to boosting the human immune system and sustaining their life. These plant leaves and trunks can also be used to effectively treat a variety of diseases in humans. The images of plants, as well as the identification of the leaf through artificial intelligence, are critical for obtaining such benefits. The proposed system automatically grades the various species and classify images of plant leaves into different families. The leaf image parameter is used to extract these properties such as colour, texture, shape, and so on. The proposed system makes use of colour and texture to form characteristics. The colour pattern for the texture uses GLCM and Shape extraction forms to extract colour information such as HSV (hue, saturation, value). The ANN (artificial neural network) algorithm is used to classify leaf images. Colour extraction, texture, and shape features, both alone and in combination, are used for classification. Using combined features yields better results than using single features. In the proposed system data set, 285 images are captured, 210 images with ANN are trained, and 75 images are used for the test set. With ANN, the system achieves an approximate 93.33% accuracy. The results were also validated with SVM (support vector machine), which provides an approximate 48% accuracy, indicating that ANN outperforms SVM. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
India first detected SARS-CoV-2, causal agent of COVID-19 in late January-2020, imported from Wuhan, China. March-2020 onwards; importation of cases from rest of the countries followed by seeding of local transmission triggered further outbreaks in India. We used ARTIC protocol based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT from Oxford Nanopore Technology to understand introduction and local transmission. The analyses revealed multiple introductions of SARS-CoV-2 from Europe and Asia following local transmission. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, here, proposed as A4-clade based on its divergence within A-cluster. The viral haplotypes may link their persistence to geo-climatic conditions and host response. Despite the effectiveness of non-therapeutic interventions in India, multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.