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Tomato Disease Detection & Fertiliser Recommendation: AI Crop Disease Detection & Recommendation

AI Crop Disease Detection & Recommendation

We built real-time tomato disease detection from images, video and live feeds, paired with fertiliser recommendations so growers know what action to take.

  • Agriculture / AgriTech
  • AI & machine learning
  • Project
Tomato Disease Detection & Fertiliser Recommendation case study

The Challenge

What stood in the way.

Growers need disease detected quickly from images or video, and actionable treatment guidance once it is found.

Speed of detection

Disease spreads, so it needs to be spotted early.

Different inputs

Growers may have photos, recorded video or a live camera feed.

Knowing what to do

Detection is only useful if it comes with guidance on treatment.

Our Solution

How we answered it.

We built real-time disease detection that works on images, video and live feeds, using YOLOv8 and OpenCV, with fertiliser recommendations presented through a Streamlit interface.

  • ProblemDisease had to be found quickly.
    Our answerWe built real-time detection with YOLOv8.
  • ProblemInputs ranged from photos to live cameras.
    Our answerWe supported images, video and live feeds.
  • ProblemGrowers needed guidance, not just a label.
    Our answerWe added fertiliser recommendations to each result.

How It Works

The parts that made the difference.

Image detection

Disease is detected in still images of plants.

Video and live-feed detection

The system detects disease in recorded video and live feeds in real-time.

Fertiliser recommendations

Each detection comes with a fertiliser recommendation.

Streamlit interface

A Streamlit app gives growers a simple way to use the model.

Results

What changed for the client.

Growers get automated disease recognition with practical next steps.

  • Automated disease recognition
  • Actionable fertiliser recommendations
  • Detection from images, video and live feeds

Technology

What it was built with.

Part of our AI and machine learning work.

  • YOLOv8
  • Streamlit
  • Python
  • OpenCV

Let’s Talk

The next big thing starts with a conversation.

An idea, a challenge, a “what if”. Tell us what you have in mind, and we’ll figure out the next step together.

What happens next

  1. Share your ideaSend the form or book a call, whichever suits you.
  2. Discovery callWe talk through your goals, timeline and scope.
  3. A clear proposalA fixed price or monthly rate before any work begins.
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