CropCare AI is an AI-powered crop health assistant that helps farmers identify possible crop diseases by uploading plant images. It provides disease insights, symptoms, possible causes, preventive measures, and farming recommendations. Farmers can also ask questions and receive guidance in English and Kannada, making smart farming support more accessible and easier to understand.
This is open-source software platform built using react and backend is under development using RUST and probably MongoDB
AgroSense is a modern agriculture management platform that leverages IoT and data analytics to help farmers monitor crop health, soil quality, and weather conditions in real-time. The system uses IoT sensors to collect field data, which is visualized on a React-based dashboard. Machine learning models predict irrigation needs and pest risks, helping farmers make data-driven decisions for higher yields.
This project describes the development of a real-time 3D vehicle tracking and visualization system that uses Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS) data from Xsens sensors. The system is intended for use in autonomous driving, vehicle dynamics studies, and navigation systems. It provides precise real-time location and orientation tracking, as well as immersive 3D visualization. The primary goals include developing a dependable sensor data collecting pipeline, providing correct vehicle orientation rendering, optimizing real-time data processing, and constructing a modular software architecture for scalability. The system, built using C++ and OpenGL, uses the Xsens Device API for sensor transmission, quaternion-based orientation computations, and GPS-to-ENU coordinate conversions, all backed by a multi-threaded real-time processing framework. The resulting system achieves 100Hz data acquisition, precise vehicle model rendering with right orientation, smooth path history visualization, real-time reference and current data presentation, and consistent performance with minimal latency. Overall, this study demonstrates successful IMU GNSS integration for real-time vehicle tracking, laying the groundwork for sophisticated autonomous navigation and monitoring systems.
An industrial-grade Automated Optical Inspection (AOI) system that compares each assembled PCB against a golden reference and detects defects in real time, directly on the production line. It combines advanced computer vision, AI-based defect classification and a touch-friendly operator HMI, and it covers component-level faults such as missing components, tombstoning, solder bridges, misalignment, insufficient solder and surface scratches.
A Smart Switch develop with rich UI developed based on LVGL framework which is free from license and freely available to develop the UI on embedded device. This project runs on ESP32-TUX hardware and use WiFi, MQTT technologies to control the digital input, output and shows data read from the analog sensors.
Project URL: https://www.kliyora.com Spending more on AWS than expected? Many organizations unknowingly waste a significant portion of their cloud budget on idle and underutilized resources. Kliyora is a cloud optimization and FinOps SaaS platform designed to help organizations gain visibility into their AWS infrastructure, identify inefficiencies, and reduce unnecessary cloud costs. It enables teams to detect idle resources, analyze usage patterns, and receive actionable recommendations for optimization. Built with a scalable multi-tenant architecture, Kliyora integrates securely with AWS using role-based access and provides insights across services like EC2, RDS, and S3. By bridging the gap between visibility and action, it helps teams move towards smarter cloud management and improved operational efficiency.
The User Analytics Dashboard is a modern desktop application built with Avalonia UI and .NET 8, designed to visualize and manage user data in real time. It integrates seamlessly with MongoDB for fast and flexible data handling, while maintaining a clean, responsive, and professional interface. Developed with the MVVM architecture, the project emphasizes scalability, design clarity, and maintainability. Interactive charts powered by OxyPlot , LiveCharts2 etc...bring insights to life, allowing users to monitor registration trends, departmental performance, and user distribution efficiently. Highlights: Cross-platform .NET 8 desktop app using Avalonia UI MongoDB backend for real-time data storage and retrieval OxyPlot/LiveCharts2-based interactive data visualizations MVVM architecture for clean separation of UI and logic Ready for IoT integration and telemetry analytics Elegant UI design optimized for clarity and performance
Developed an AI-powered platform to analyse past sales data, predict trends, and provide real-time business insights. Integrated data visualization and market-based recommendations to support strategic decision-making.