🚀 Key Objectives

• • Reduce dependence on intermediaries by enabling direct farmer-to-market access
• • Provide AI-driven, personalized recommendations for crop selection, irrigation, and pest management
• • Introduce a CIBIL-like Farmer Credit Score to help banks, insurers, and lenders assess financial eligibility
• • Leverage satellite imagery and environmental data for predictive analytics and yield optimization
• • Deliver a multilingual (Hindi + English) chatbot interface for easy adoption by farmers

🛰️ Core Features

🔹 Multi-Agent AI System

🔹 Farmer Credit Scoring System

300–900 scale credit rating, similar to CIBIL.

🔹 Smart Marketplace

• Multi-image product uploads with preview
• Verified seller and buyer profiles
• Real-time commodity price updates
• B2B/B2C support for both farmers and enterprises

🔹 Business Intelligence & Analytics

• Price Forecasting: Real-time commodity trends and seasonal demand
• Procurement Insights: Optimal purchase strategies for agri-businesses
• Seller Verification: Automated trust scoring system
• Dashboards: Interactive visualization of agricultural metrics

🛠️ Technology Stack

📊 Impact & Results

🌍 Vision & Future Roadmap

• 📱 Mobile-first app for rural adoption
• 🔊 Voice Assistant in Hindi & English for accessibility
• 🖼️ Computer Vision Models for pest/disease detection from farmer-uploaded images
• ☁️ Cloud-based scalability for pan-India adoption
• 🌐 WhatsApp integration for farmer-friendly access

Project Details

The project aimed to improve the precision of tumor detection and segmentation in medical imaging using state-of-the-art deep learning techniques:

• Attention Gates in U-Net: Implemented attention mechanisms to focus on relevant features in medical images
• Grad-CAM Integration: Added explainability to the model using Gradient Class Activation Mapping
• Research Publication: Authored a paper detailing the methodology and results
• Performance Metrics: Achieved 92% accuracy in tumor classification and 88% Dice score in segmentation tasks
• Dataset: Used a diverse dataset of MRI scans to ensure robust model training and validation

The research has potential applications in clinical diagnosis, surgical planning, and treatment monitoring.

Project Details

This project combines computer vision and web development to create a comprehensive attendance solution:

• Face Detection and Recognition: Implemented using OpenCV and deep learning models
• Web Interface: Built with React.js for a responsive and intuitive user experience
• Database: MongoDB for storing student records and attendance data
• Analytics Dashboard: Visual representation of attendance patterns and statistics
• Real-time Updates: Instant feedback and notifications for attendance marking

The system reduces manual effort, eliminates proxy attendance, and provides valuable insights through attendance analytics.

Project Details

The AI chatbot leverages advanced NLP techniques to deliver a seamless conversational experience:

• Intent Recognition: Identifies the purpose behind user queries
• Entity Extraction: Recognizes important information like dates, names, and product references
• Contextual Understanding: Maintains conversation context for more natural interactions
• Multi-language Support: Handles conversations in English, Hindi, and Tamil
• Integration Capabilities: Can be deployed on websites, mobile apps, and messaging platforms

The chatbot reduces response times, handles routine inquiries automatically, and improves customer satisfaction rates.

Project Details

This web application provides a centralized platform for managing all aspects of an educational institution:

• Student Management: Registration, profile management, and academic tracking
• Faculty Portal: Course management, grade submission, and student performance analytics
• Administration Dashboard: Institutional metrics, department management, and reporting tools
• Communication Tools: Announcements, messaging system, and notification center
• Technology Stack: MERN (MongoDB, Express.js, React.js, Node.js)

The system improves operational efficiency, reduces paperwork, and enhances communication across the institution.

Project Details

This project focuses on understanding the core principles of machine learning through implementation:

• Algorithms Implemented: Linear Regression, Logistic Regression, Decision Trees, K-Means Clustering, and Neural Networks
• Optimization Methods: Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent
• Evaluation Metrics: Custom implementation of accuracy, precision, recall, F1-score, and ROC curves
• Visualization Tools: Interactive plots and diagrams to explain algorithm behavior
• Educational Component: Comprehensive documentation and explanations of mathematical foundations

This project demonstrates a deep understanding of machine learning fundamentals and computational implementation techniques.

Project Details

The language model training project explored advanced techniques in NLP and transfer learning:

• Base Models: Used transformer-based architectures like BERT, GPT, and T5
• Fine-tuning Approach: Parameter-efficient training with techniques like LoRA and Adapter Layers
• Domain Adaptation: Specialized for educational content and technical documentation
• Evaluation Framework: Custom metrics for measuring domain-specific performance
• Deployment Strategy: Optimized for efficient inference on resource-constrained environments

The fine-tuned models demonstrate significant improvements in domain-specific tasks compared to general-purpose language models.