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Portfolio Details

HomeportfolioAdvanced Plant Disease Detection Tool Using Computer Vision
  • Category:
    Artificial Intelligence
  • Software:
    Machine Learning , Computer Vision
  • Clients:
    Mr. Esther Howard
  • Locations:
    6391 Elgin St. Celina, UK
  • Date:
    23/03/2024
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Advanced Plant Disease Detection Tool Using Computer Vision

Client Overview

Farmers lose billions of dollars due to late or inaccurate disease detection. To solve this, we created an AI-powered. Plant Disease Detection Tool using Computer Vision, Deep Learning, and Machine Learning. The system identifies plant diseases from leaf images with high accuracy and provides actionable recommendations.

 

Client Requirements

  • Detect plant diseases from photos automatically
  • Support multiple crop types
  • Provide confidence scores and explanations
  • Suggest treatments and preventive measures
  • Work online and offline
  • Deploy on mobile and web
  • Scale to thousands of users
  • Maintain high real-world accuracy

Solution Provided

We built a complete computer vision pipeline involving preprocessing, deep learning classification, severity analysis,
and treatment recommendations. The system uses CNNs, transfer learning, image augmentation, real-time inference APIs,
and explainable AI techniques.

How the System Works

Image Acquisition & Preprocessing

Users upload a leaf image. The system:- Resizes image- Normalizes pixels- Removes noise- Adjusts lighting- Detects the leaf region

Data Pipeline & Augmentation

To improve robustness:- Rotation- Zoom- Flip- Lighting adjustments- Blur- Background changes

Deep Learning Model

Models tested:- ResNet50- InceptionV3- EfficientNet-B0- MobileNetV3 (for mobile)Final model: EfficientNet-B0 for speed + accuracy.

Disease Classification

The model predicts:- Disease name- Confidence score- Severity level

Supported conditions include:- Early blight- Late blight- Leaf curl- Mildew- Mosaic virus- Bacterial spot- Healthy leaf state

Explainability (Grad‑CAM)

The system highlights infected regions so users understand the prediction visually.

Recommendation Engine
Recommendation Engine

After classification, the system provides:- Treatment steps- Organic and chemical options- Preventive strategies- Soil/water adjustmentsData based on:- Rule-based engine- Expert-verified agricultural knowledge

Deployment & Integration
Deployment & Integration

Tools used:- FastAPI backend- TensorFlow Lite mobile models- Docker containers- AWS EC2 and Lambda- CDN acceleration

Testing & Evaluation

94%–97% classification accuracy

120–200 ms mobile inference

<1 second web inference

Robustness tests across lighting, soil background, blur, and occlusion.

Impact & Results Of The Project

  • Accurate disease detection
  • Real-time analysis
  • Early identification to prevent loss

Conclusion

This project highlights expertise in:
– Computer Vision
– Deep Learning
– Agricultural AI solutions
– Mobile and cloud deployment
– Real-time inference systems
– Explainable AI

The solution empowers farmers, reduces losses, and modernizes agricultural monitoring.