Plant Identification ++ vs Gel

Identify plants in a snap. Take a picture with iPhone's camera, or pick a photo from your library, then we'll use advanced machine learning algorithm to identify the plant in the picture. It's convenient and fast! Whether you're working in your garden, or hiking on the sunny days; whenever you want to figure out what's the name and details of a beautiful flower or an unknown grass, simply use this app to take a picture, and we'll tell you the details instantly. It is very convenient for the curious minds, or people who want to decorate their own gardens with a specific plant. Key Features: - Instantly identify tens of thousands of plants, flowers, trees, leaves - Powered by advanced artificial intelligence - A large database of plants - Great for gardening fans and horticulture specialists - Super simple interface, easy-to-use for anyone - Suggest a list of possible plants based on your photo - Show details about a plant - Check your identification history at any time - Share your picture with plant details to your friends Future Features: - A community for plant lovers - Plant care tips - You can subscribe for premium features from inside the app; subscriptions are billed monthly or annually at the rate selected depending on the subscription plan. - Alternatively a one-time payment plan is available (this is not a subscription). - Payment will be charged to your iTunes account at confirmation of purchase. - Subscriptions will automatically renew unless auto-renew is turned off at least 24 hours before the end of current period. - Your account will be charged according to your plan for renewal within 24 hours prior to the end of the current period. - You can manage or turn off auto-renew in your Apple ID account settings at any time after purchase: Go to Settings > iTunes & App Store; Tap your Apple ID at the top of the screen; Tap View Apple ID. You might need to sign in or use Touch ID; Tap Subscriptions; Tap the subscription you want to manage; Use the options to manage your subscription. - No cancellation of the current subscription is allowed during the active subscription period. Once purchased, refunds will not be provided for any unused portion of the term. Privacy Policy: https://plant-identification.flycricket.io/privacy.html Terms of Use: https://plant-identification.flycricket.io/terms.html Connect with us: https://twitter.com/PlantStoryApp

To understand community-specific water quality index, it is not enough to have data based on water quality parameters only. An increase in anthropogenic activity, population growth, and urbanization have a significant impact on all natural resources, particularly water quality, which is worsening. Along with all of these scenarios, extreme variability in weather patterns aggravates the situation by exponentially increasing the crisis to the access, availability, and affordability of clean drinking water solutions. This is becoming a major concern and crisis. Another major challenge is not having access to real-time information on dynamic variability in water contamination levels and its impact on public health, which contributes to a lack of awareness among multiple stakeholders. Hence multiple stakeholders are not able to ensure safe drinking water availability since they lack real-time knowledge of dynamical geo-spatial variation of water quality and its impact. Therefore, climatic variations along with the terrain and environmental factors lead to a dynamic spatio-temporal impact on regional water quality. Indeed the above challenges have prompted us to study all the Water Quality Index (WQI) impact factors such as terrain features, climatic variations, anthropogenic activities, agricultural practices, etc., that affect the water quality and develop a solution that could determine dynamically varying zonal water quality indices. Water quality variability of different sources will be studied by employing multiple data monitoring techniques such as remote sensing, in-situ monitoring, and so on. The GEL-IoT framework is capable of disseminating the data captured using such monitoring techniques and thereby empowering the community members and other stakeholders to be ‘water-wise’. It enables the identification of the predominant contaminants, and the diseases caused by them which the water authority or decision-makers could use to take corrective measures for a specific location. All the information collected by the GEL-IoT framework will aid the design and development of AI-based decision models integrating the geospatial models (GeoAI models) and community-centric business models which helps in the sustainability of the water purification system and formation of water-wise communities.