Radio frequency identification (RFID) is one of the most promising and anticipated technologies in recent years. The paper proposes an automated system for waste collection and container monitoring system using RFID, GPS, GIS and GSM technologies. The proposed system consists of RFID tags mounted on containers, RFID readers mounted on trucks along with GPS for location tracking and GSM module for wireless transmission. The system provides real time monitoring of the waste collection system through a web based application available to administrators for decision making like reallocation of routes and containers etc. and management issues like observing performance of contractors, observing waste generation characteristics of particular area etc. and to the citizens providing transparency in civic administration. The model is proposed for Ahmedabad Municipal corporation.

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<p><strong>Abstract.</strong> Citizen science has emerged as a game changer in various scientific endeavors, wherein scientific data for understanding the phenomenon could be collected by volunteers/non-specialist in a quick possible time. Citizens nowadays play an important role by functioning as “sensors” helping government/institutions by collecting and analyzing data. The advancements and convergence of technologies (Information and communication technologies (ICT)), especially the Internet and mobile technology has further assisted in such efforts. Moreover, the location sensors (GPS) and camera on board the mobile devices enables citizens to collect geotagged data. The classic example is the OpenStreetMap project where volunteers contribute towards the mapping of the planet. This paper highlights the geospatial solution based on citizen science to collect geotagged data about the water quality (turbidity). This solution is developed using open source tools and consists of an Android based mobile app and web based dashboard on the server side for real time data visualization and analysis. The web application is designed and developed using PHP, JavaScript, HTML &amp; CSS and allows user to view the interpolated geotagged data about water quality over various background maps like OSM, Bhuvan etc. PostgreSQL/PostGIS are used as the backend geospatial data server for storing the geotagged dataset. Such solution will be very useful for water quality monitoring as part of national level project like Clean Ganga Mission using the citizen centric approach.</p>

Abstract-Automated system for plant species recognition is need of today since manual taxonomy is cumbersome, tedious, time consuming, expensive and suffers from perceptual biasness as well as taxonomic impediment. Availability of digitized databases with high resolution plant images annotated with metadata like date and time, lat long information has increased the interest in development of automated systems for plant taxonomy. Most of the approaches work only on a particular organ of the plant like leaf, bark or flowers and utilize only contextual information stored in the image which is time dependent whereas other metadata associated should also be considered. Motivated from the need of automation of plant species recognition and availability of digital databases of plants, we propose an image based identification of species of plant when the image may belong to different plant parts such as leaf, stem or flower, fruit , scanned leaf, branch and the entire plant. Besides using image content, our system also uses metadata associated with images like latitude, longitude and date of capturing to ease the identification process and obtain more accurate results. For a given image of plant and associated metadata, the system recognizes the species of the given plant image and produces an output that contains the Family, Genus, and Species name. Different methods for recognition of the species are used according to the part of the plant to which the image belongs to. For flower category, fusion of shape, color and texture features are used. For other categories like stem, fruit, leaf and leafscan, sparsely coded SIFT features pooled with Spatial pyramid matching approach is used. The proposed framework is implemented and tested on ImageClef data with 50 different classes of species. Maximum accuracy of 98% is attained in leaf scan subcategory whereas minimum accuracy is achieved in fruit sub-category which is 67.3 %.

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