Project Category: Development

Deep Learning Approach for Urban Mapping and Analytics This project is an AI-powered web application that analyzes satellite imagery to detect vegetation coverage, urban heat islands, air quality, and high-rise buildings in rapidly growing cities. By combining data from Sentinel-2, Sentinel-5p,…

The AI Trading Bot is a next-generation trading system that integrates structured data such as OHLC, volume, and technical indicators with unstructured data like financial news, social media sentiment, and analyst reports. Using early fusion at the feature level and late fusion at the decision level, the bot generates accurate buy/sell signals while reducing risk exposure. Retail traders can access live dashboards, portfolio insights, and risk alerts, while quant researchers and institutions can backtest, refine, and scale fusion-driven strategies. The platform includes explainable AI, showing which inputs influenced predictions, and a strategy builder that lets users balance early vs. late fusion weights. With built-in risk management (stop-loss, trailing stop, position sizing) and seamless API integration into brokers and exchanges, the bot is designed for real-world deployment. Outcomes include improved trading accuracy, robust risk control, and faster strategy research cycles for both retail and institutional users.

This AI-driven system uses drone imagery, machine learning, and computer vision to detect lodging and weed infestations in sugarcane fields. High-resolution RGB and multispectral data feed deep-learning models (CNN/U-Net) that identify flattened canopies, bent stalks, and weed patches, generating severity maps and interactive heatmaps. Vegetation indices (NDVI/SAVI) help separate sugarcane rows from unwanted plants and quantify stress signals for precision interventions. Farmers, agronomists, and sugar mills can visualize risks, receive alerts, and export maps to guide targeted spraying and harvesting logistics—even offline in rural areas. Historical trend analysis supports treatment validation and planning, while web and mobile dashboards streamline decisions across large estates. The result: earlier detection, reduced harvesting losses, optimized herbicide use, and lower manual scouting costs—at scale.

GAN-based Mango Variety & Disease Detection This AI/ML research project applies Generative Adversarial Networks (GANs) and computer vision to classify mango varieties and detect diseases from leaf and fruit images. It enables farmers, researchers, and agri-businesses to ensure authenticity, monitor crop…

Smart Diagnostics – Tractor Safety & Quality AI Tracker Smart Diagnostics – Tractor Safety & Quality AI Tracker is a mobile application built for agricultural tractors to enhance safety and measure task quality through AI and telematics. The platform ensures safer…

Telematics Supply Chain Compliance & Intelligence Telematics Supply Chain Compliance & Intelligence is an end-to-end logistics platform that equips fleets with GPS trackers, IoT sensors, and AI analytics to ensure delivery compliance, driver safety, and operational optimization. The solution currently scales…

CropSight — Smart Agriculture & Agro-Industry Management CropSight is an end-to-end platform for agricultural operations and agro-industry projects (e.g., sugar mills, wheat mills). It unifies planning, field execution, monitoring, and reporting across farms, contractors, and mill management—so decisions are made on…