Built with Purpose,
Powered by Science
A deep learning project dedicated to helping farmers and gardeners protect their crops
Why We Built LeafSense
Food security starts with healthy plants
Plant diseases threaten global food security, causing billions of dollars in crop losses annually. Early and accurate detection is crucial for effective treatment, but expert pathologists aren't always available â especially in rural communities.
LeafSense bridges this gap by putting the power of deep learning into a simple web interface. Upload a photo of a plant leaf, and our AI model identifies the plant species and diagnoses potential diseases in seconds â providing actionable treatment recommendations.
What Makes LeafSense Special
Designed with real-world needs in mind
Privacy First
Images are processed on the server and deleted immediately. No data is stored or shared with third parties.
Open Source
Fully open-source codebase. View, contribute, and learn from the code on GitHub. Built for the community.
Completely Free
No fees, no subscriptions, no hidden costs. Accessible to everyone - from students to small-scale farmers.
Explainable AI
Transparent predictions with top-3 results, confidence scores, and detailed disease information for each diagnosis.
Technology Stack
Built with modern, industry-standard tools
đ§ Backend
đ§ Machine Learning
đ¨ Frontend
đ Data & Training
đ Deployment
Project Objectives
Deep Learning project milestones
Dataset Collection & Preparation
Source the PlantVillage Dataset from Kaggle and preprocess 54,305 images across 38 classes.
Custom CNN Development
Design and train a baseline CNN architecture from scratch to establish performance benchmarks.
Transfer Learning (ResNet50)
Implement ResNet50 with custom classifier head, achieving ~99.5% accuracy through fine-tuning.
Model Evaluation & Comparison
Compare Custom CNN vs ResNet50 using accuracy, confusion matrices, and classification reports.
Application Interface
Build a responsive, multi-page Flask web application with intuitive drag-and-drop upload interface.
Agricultural Knowledge Base
Curate disease information including severity levels, treatment plans, and prevention strategies.
Backend Integration & Deployment
Integrate trained model with Flask backend and prepare for production deployment.
Meet the Developer
The mind behind LeafSense
Developer & Creator
ÂĨ@$# Kakadiya
Machine Learning Engineer & Full-Stack Developer
Passionate about leveraging technology to solve real-world problems.LeafSense was born from the intersection of desire to learn deep learning and a commitment to make agricultural insights accessible to everyone.
Explore the Project
Resources and links
Important Disclaimer
LeafSense is a hobby project developed for educational and research purposes. It is not a substitute for professional agricultural consultation. While the model achieves high accuracy on the test dataset, real-world conditions (lighting, camera quality, leaf angle) may affect results. Always consult a qualified agronomist or plant pathologist for definitive diagnosis and treatment decisions.
Try LeafSense Now
Upload a leaf image and experience AI-powered plant disease detection
đŦ Start Diagnosis