AaradhyaNikam/Agri-Vision-IIT-MANDI-PROJECT

# 🌱 Jaaniv Krushi: AI Crop Disease Detection System An **AI-powered agricultural diagnostic tool** that utilizes deep learning to classify plant diseases from the PlantVillage dataset and leverages Google's Gemini AI to provide actionable treatment and prevention strategies. --- ## 💡 Problem Statement Farmers frequently lack immediate access to agricultural experts to identify crop diseases, leading to reduced yields. **Jaaniv Krushi** bridges this gap by combining computer vision for real-time disease detection with generative AI to offer instant, localized, and organic treatment plans. --- ## 🚀 Key Features & Architecture **Data Flow:** `Leaf Image Input` ➔ `Image Preprocessing` ➔ `Keras CNN Inference` ➔ `Disease Classification` ➔ `Gemini AI Prompting` ➔ `Streamlit UI` * **Deep Learning Vision:** Accurate image classification powered by a custom-trained Keras model. * **Generative AI Advice:** Integrates Google Gemini API to generate context-aware treatment steps and organic solutions. * **Interactive Dashboard:** A clean, user-friendly interface built entirely in Streamlit. * **Jupyter Research Environment:** Includes full model training, data processing, and inference research in `Code.ipynb`. --- ## 📸 System Previews ### Disease Detection & Accuracy ### Treatment Steps ### Prevention Methods ### Organic Solutions --- ## 🛠️ Tech Stack **Language:** Python 3.12+ **Machine Learning:** TensorFlow, Keras, NumPy, PIL **Generative AI:** Google Gemini AI API **Frontend & Deployment:** Streamlit **Environment:** Jupyter Notebook, Windows PowerShell --- ## ⚙️ Local Setup & Run (Windows PowerShell) It is recommended to run this project in a virtual environment. All terminal commands are combined below for easy setup. powershell # 1. Clone the repository and navigate into it git clone [github.com] cd jaaniv-krushi # 2. Create and activate a virtual environment python -m venv .venv .\.venv\Scripts\Activate …