PlantGuard AI: Open-Source Plant Disease Detection That Gets You Outside to Touch Grass 🌿

A developer built PlantGuard AI, an open-source plant disease screening tool that fine-tunes a MobileNetV2 model on the PlantVillage dataset to classify 38 leaf conditions and pairs each prediction with a human-reviewed care profile. The project runs inference locally, storing scan history and notes in a SQLite journal, and nudges users toward outdoor plant inspections rather than extended screen time. It was submitted to the Hacktoberfest Open-Source AI Challenge Week 1 "Touch Grass" and is available on GitHub with a browser-based demo.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 . GitHub Repository: PlantGuard AI on GitHub https://github.com/lalit-oli-mohan-479/PlantGuard-AI---Open-Source-Plant-Disease-Detector Live Demo: Try PlantGuard AI https://lalit-oli-mohan-479.github.io/PlantGuard-AI---Open-Source-Plant-Disease-Detector/ In an age where developers and tech workers spend 8 to 10 hours a day glued to glowing screens, our connection with the physical, living world can easily deteriorate. When a beloved balcony tomato plant develops yellow halos or an apple tree shows olive-green spots, our modern reflex is to grab a phone, search endlessly, and sink deeper into screen fatigue. Existing commercial plant identification apps can make this experience even more frustrating: PlantGuard AI was built to offer a different approach. Created for the Hacktoberfest Open-Source AI Challenge Week 1: “Touch Grass,” PlantGuard AI is an open-source plant disease screening tool and garden care companion designed to run locally. It uses open-weight computer vision not to keep you glued to your screen, but to help you step away from your desk, get some sunlight, and reconnect with your garden. 🌱 Explore the project: GitHub Repository https://github.com/lalit-oli-mohan-479/PlantGuard-AI---Open-Source-Plant-Disease-Detector PlantGuard AI uses a MobileNetV2 architecture fine-tuned on the PlantVillage dataset to identify visual patterns associated with plant diseases and healthy leaves. These results represent the project's reported benchmark performance, not a guarantee of accuracy for every real-world garden condition. PlantGuard AI treats AI predictions as probabilistic screening aids, not definitive diagnoses . Each of the 38 classes includes a human-reviewed care profile designed to help users understand symptoms and choose sensible next steps. Profiles include: Recommendations are intended as general gardening guidance. Treatments should be selected according to the plant, likely cause, and local growing conditions. This is the heart of PlantGuard AI. Instead of ending with an AI prediction, the application gives users a customized checklist that encourages them to inspect their plants outdoors. Interactive Milestone Rewards: After completing the outdoor inspection, users can click “Mark Outdoor Inspection Complete” to record their garden visit and celebrate time spent caring for living plants. The goal is simple: use AI as a bridge to real-world action, not as another reason to stay indoors. The desktop application stores scan history, prediction confidence, personal notes, and outdoor follow-up statuses in a local SQLite database: data/journal.db Features include: Pending Outdoor Check , Grass Touched: Inspected Outdoors , and Recovering . Privacy by design: The local desktop workflow is designed to keep plant photos, notes, and inference on the user's machine rather than uploading them to third-party servers. The browser-based showcase uses browser localStorage for its client-side journal, which is separate from the desktop application's SQLite database. Experience PlantGuard AI in two ways. Try it here: Open the Interactive GitHub Pages Demo https://lalit-oli-mohan-479.github.io/PlantGuard-AI---Open-Source-Plant-Disease-Detector/ You can explore the interactive showcase from a phone, tablet, or desktop browser. What you can do: localStorage . Note: The browser showcase and the full local desktop application are separate experiences. Check the repository documentation for the implementation details and capabilities of each. Run the Python application locally to explore the complete desktop workflow. Prerequisites Quickstart Commands 1. Clone the repository git clone https://github.com/lalit-oli-mohan-479/PlantGuard-AI---Open-Source-Plant-Disease-Detector.git 2. Enter the project directory cd PlantGuard-AI---Open-Source-Plant-Disease-Detector 3. Install dependencies pip install -r requirements.txt 4. Launch the local application python app.py Get the code: View the PlantGuard AI GitHub Repository https://github.com/lalit-oli-mohan-479/PlantGuard-AI---Open-Source-Plant-Disease-Detector PlantGuard AI is built around a simple idea: technology should help us engage more meaningfully with the world around us. By combining accessible computer vision, transparent prediction confidence, local data storage, and practical gardening checklists, the project aims to make plant health screening more approachable while encouraging users to spend less time scrolling and more time caring for their plants. It is not about replacing hands-on gardening expertise. It is about using AI to help people ask better questions, inspect their plants more carefully, and take informed next steps. PlantGuard AI is an open-source project, and contributions are welcome. 🌱 GitHub: Star the repository, explore the code, or contribute https://github.com/lalit-oli-mohan-479/PlantGuard-AI---Open-Source-Plant-Disease-Detector 🌐 Live Demo: Try PlantGuard AI https://lalit-oli-mohan-479.github.io/PlantGuard-AI---Open-Source-Plant-Disease-Detector/ Built for Hacktoberfest. Inspired by nature. Designed to bring people back to their gardens. Touch grass. Inspect a leaf. Let AI help you take the next step. 🌿