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    Fully Automated Waste Management System Using Line Follower Robot
    (Springer Science and Business Media Deutschland GmbH, 2022) Geetha, V.; Salvi, S.; Ghosh, S.K.; Ahmed, S.S.; Meshram, R.S.
    With a population of over seven billion which generates waste of more than two billion metric tons a year, waste management is a serious issue that needs to be addressed. All this waste needs to be managed so that there will not be an overflow at the waste disposal bins in a locality as that might lead to deadly diseases and pollution. To overcome this problem, in this paper, we propose a way to collect the waste automatically using a line follower robot and dump it in the dumping ground. The proposed system uses an Arduino Yun which is installed on top of the line follower and a NodeMCU, which is installed at the garbage disposal sites for communication and collection of garbage. Both these components communicate over the “ThingSpeak†Cloud. These bins continuously send the percentage of waste that is in the bin. When the percentage reaches a certain threshold, the line follower goes to the site and collects the garbage and dumps it at a nearby dumping yard. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Classification of Medicinal Plants Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Meshram, R.S.; Patil, N.
    Nowadays, peoples are not having information about the surrounding plants and their medicinal values. If some person wants to know about the medicinal plants, they have to contact the person who is having deep knowledge about the medicinal plants and its uses. In order to solve this problem we can use the current technology to give a tool which will help the common people to know more about the medicinal plants. For doing this we can use many machine learning techniques for classifying the medicinal plants with more accuracy. Different kind of medicinal plant species are available on the planet earth but classification of the Particular medicinal plant is very difficult without knowing about the plants first. The information about the medicinal plants is collected by the scientists and urban people. Generally this kind of knowledge is passed through generation to generation and sometimes there might be some changes in the information and its contents. So according to the current situation we can use the machine learning technology to make the tool which will be helpful to solve the medicinal plant classification problem. Machine learning model can easily classify the medicinal plants after the feature extraction and applying the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.