RashtraRakshak: An AI-Driven Advisory and Mentorship Platform for Farmers, Soldiers, and Citizens
Abstract
Rashtra Rakshak is an AI-powered web-based platform developed to support farmers, soldiers, and citizens through a unified digital system. The platform provides farmers with AI-based crop advisory services and plant identification using a CNN-based image analysis module. Soldiers contribute structured mentorship content by uploading training videos related to survival skills, disaster preparedness, and safety awareness, which are made accessible to citizens for learning and awareness. The proposed platform addresses these challenges by offering role-based dashboards supported by artificial intelligence. Farmers receive AI-based crop advisory and plant identification using a convolutional neural network (CNN) image analysis module. Soldiers contribute mentorship and training videos related to survival skills, disaster preparedness, and safety awareness, which are made accessible to citizens. A multilingual chatbot assists users with agricultural queries, safety guidance, and system navigation in both English and local languages. An emergency SOS module enables location-based alert reporting for enhanced public safety. The system also integrates a multilingual chatbot that assists users with agricultural guidance, safety-related queries, and platform navigation in simple local languages. RashtraRakshak is implemented as a working Minimum Viable Product (MVP) using Django, Python, and web technologies, with a focus on ease of use, accessibility, and real-world applicability. By combining advisory services, visual analysis, mentorship learning, and conversational assistance into a single platform, the project demonstrates how AI can be effectively used for community support without reliance on complex hardware or external sensor systems. RashtraRakshak is implemented as a working Minimum Viable Product (MVP) using Django, Python, and web technologies, with a strong focus on usability, accessibility, and real-world deployment. The platform demonstrates how AI-based advisory systems, computer vision, and digital mentorship can be combined effectively without relying on IoT devices or complex hardware infrastructure.
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