Hammad Tariq@hammad_tariq17

PlantAI is an agentic computer vision system designed to help farmers, agricultural students, researchers, and plant-care professionals identify plant diseases quickly and make more informed decisions. Plant disease diagnosis is often dependent on manual inspection or expert knowledge, which can be slow, subjective, and difficult to access—especially when symptoms look similar across different diseases. A wrong or uncertain diagnosis can lead to inappropriate treatment, crop loss, and unnecessary use of pesticides. PlantAI addresses this problem through an AI-powered diagnosis pipeline that combines computer vision with agentic evidence verification. Users interact with a simple interface by uploading an image of a plant leaf. The computer vision model analyzes the image and predicts the most likely disease. Instead of relying only on a single classification result, PlantAI uses an ensemble of deep learning models to improve prediction reliability. The system combines EfficientNetV2-S, ConvNeXt-Tiny, and Swin-Tiny through weighted ensemble learning. A supervisor agent then coordinates additional evidence-gathering steps, including retrieval-augmented generation (RAG), disease-specific knowledge retrieval, web research, and contextual information. The collected evidence is analyzed and fused with the computer vision prediction to provide a more explainable and reliable diagnosis. A key feature of PlantAI is its focus on evidence-backed AI rather than simply returning a disease label. The system can provide the predicted disease, confidence information, supporting evidence, and relevant diagnostic context. When the available evidence is insufficient or the prediction is uncertain, the architecture can support uncertainty handling and abstention rather than forcing an unreliable answer. What makes PlantAI unique is the combination of high-performance computer vision, ensemble learning, RAG, web research, and agentic verification in a single plant-disease diagnosis workflow. This creates a multi-stage system where visual recognition is supported by external evidence and reasoning rather than operating as an isolated image classifier. The target users can upload a plant image through the web application and receive an AI-generated diagnostic response in an accessible, conversational format. PlantAI is designed to make advanced AI-assisted plant disease analysis more accessible while providing a foundation for future agricultural decision-support applications. PlantAI demonstrates how computer vision and agentic AI can work together to transform image-based disease recognition into a more evidence-aware and reliable diagnostic workflow.

TripAI is an AI-powered flight and travel planning agent developed by Team Infinite Mind for the AI Factory Hackathon. It transforms fragmented travel planning into one intelligent natural-language conversation. Using LangGraph, Groq, FastAPI, Aviationstack, and Tavily, TripAI understands user travel requests, extracts requirements, retrieves relevant flight and travel information, discovers hotel options, and generates an organized AI travel plan. Its modern Next.js interface brings the complete experience together, making travel planning smarter, faster, simpler, and more convenient for users.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.