An ML-based Agrotech product to help farmers grow more!
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Our project idea came into existence when we came to know that 85 of the total freshwater in India and around the globe is Used used freshwater in India and around the globe in irrigating crops. Still, the fundamental problem is that as much as 60 percent of the water it waste is diverted or pumped for irrigation. Thus, we decide to develop a product that will tackle this issue when we launch the product. As resources are getting depleted every year exponentially, we can assume that strict laws and regulations for the efficient use of resources in agriculture might appear in the coming years, and to stand with them, technologies need to be used in agricultural processes. We aim the project at farmers in developing and underdeveloped countries, where the availability of agricultural resources and plant education is limited to non-existent. We will bring the project to the market by first completing the PCB prototype of the module and sensor. Then after deciding on the housing and preparing the prototype design for manufacturing, we can mass-produce and distribute the device. At the same time, the web/mobile application can be accessed from anywhere.
We have conducted user experience surveys and interviews with farmers to figure out the pain points and problems faced by them. We have created User Personas, problem statements, user story, empathy map, and if/then statement. Competitive analysis has been used to find missing features from the product. We used the bridge-gap method for overcoming the problem and approaching the solution. These all design and user experience practices allowed us to explore the problems faced by farmers and what they actually need. Specific Tech stack has been to minimize the cost of the product and make the subscription affordable. Microsoft Azure allowed us to integrate the app services, SQL database, ML studio, ASP.NET core framework, Blazor framework and Twilio messaging service, all in one. This made a whole cloud functioning and deployed web application with an admin and developers working at a single cloud service. The cost estimations for the same were calculated and compared with other services like GCP and AWS.
