Plant Doctor AI

Our mission is to democratize plant disease detection by making advanced AI technology accessible to farmers, gardeners, and agricultural professionals worldwide. We believe that early and accurate disease identification is crucial for maintaining healthy crops and ensuring global food security.

Through innovative computer vision and deep learning techniques, we aim to reduce crop losses, minimize pesticide usage, and support sustainable agricultural practices that benefit both farmers and the environment.

Our platform is built using state-of-the-art deep learning technologies. The core AI model utilizes a ResNet18 architecture, a proven convolutional neural network that excels at image classification tasks. This model has been specifically trained on thousands of plant leaf images to recognize various diseases with high accuracy.

The backend is powered by Python and Flask, providing a robust and scalable server infrastructure. Image processing is handled through OpenCV and PIL, ensuring optimal image preparation for AI analysis. The frontend delivers an intuitive user experience through modern HTML5, CSS3, and JavaScript technologies.

Our AI model achieves an impressive 99% accuracy rate across all supported plant diseases. The system can identify 38 different disease classes spanning 15 major plant species including tomatoes, potatoes, corn, apples, grapes, and many others.

The model processes images in real-time, typically providing results within 2-3 seconds. It has been trained on over 60,000 high-quality plant images and continuously improves through additional data collection and model refinement.

Plant Doctor AI is developed by Debottam Ghosh, a skilled machine learning enthusiast, combining expertise in artificial intelligence, agriculture, and software development. He specializes in computer vision and deep learning, while also researching about plant diseases and agricultural practices.

He focuses on creating user-friendly interfaces and reliable backend systems that make advanced AI technology accessible to users regardless of their technical background.

Our AI system follows a sophisticated multi-step analysis process:

1. Image Preprocessing: Uploaded images are automatically resized, normalized, and enhanced to optimize them for AI analysis. This ensures consistent input quality regardless of camera type or lighting conditions.

2. Feature Extraction: The ResNet18 model extracts thousands of visual features from the leaf image, including color patterns, texture details, spot characteristics, and leaf structure anomalies.

3. Classification: These features are processed through our trained neural network, which compares them against learned patterns from our extensive disease database.

4. Results & Recommendations: The system provides the most likely disease diagnosis with confidence scores, along with detailed information about symptoms, causes, treatment options, and prevention strategies.

Plant Doctor AI represents a significant step forward in precision agriculture and sustainable farming practices. By enabling early disease detection, we help farmers:

• Reduce Crop Losses: Early intervention can prevent diseases from spreading, saving entire harvests.
• Optimize Pesticide Usage: Targeted treatment recommendations reduce unnecessary chemical applications.
• Improve Yield Quality: Healthier plants produce better quality crops with higher market value.
• Save Time & Resources: Quick AI diagnosis eliminates the need for time-consuming expert consultations.
• Support Sustainable Practices: Precision agriculture techniques promote environmental conservation.

Our vision extends beyond individual farmers to support global food security initiatives and contribute to the United Nations' Sustainable Development Goals related to zero hunger and sustainable agriculture.