Session 1: Counting Plants on a Farm using Drone Imagery and AI, ML models

## Details \#\# Series of 5 Technical Sessions Ever wondered how farmers can instantly know exactly how many healthy plants they have across large farmlands? In this 5-part technical series, we will walk step-by-step through building and deploying an AI-powered solution that does exactly that — using nothing but drone imagery and cutting-edge Computer Vision. ### **What You’ll Learn** Across these sessions, we will: 1. **Ingest and Process Drone Imagery** – Understand how to prepare raw aerial images for AI analysis. 2. **Mango Plant Detection** – Train a custom Computer Vision model to identify which plants are mango plants. 3. **Image-wise Plant Presence** – Determine if a given image contains a mango plant and automatically add it to a running count. 4. **Geotagging Plant Locations** – Extract GPS coordinates from drone images to map plant locations accurately on the farm. 5. **Model Deployment** – Deploy the model as a production-ready application so it can be used by farmers, researchers, or agri-businesses in the field. ### **Why This Matters** This solution empowers farmers to: * Monitor plant count and detect missing plants. * Plan resources (fertilizers, water, etc.) more efficiently. * Track farm health and yield potential with minimal manual work. By the end of this series, you’ll have built and deployed an AI-based plant counting system that can work on **real farms, at scale** — helping bridge technology and agriculture for smarter farming.