Projects/Agriculture/Tamouz Agriculture Chatbot
Case Study

Tamouz Agriculture Chatbot

An agronomist in every farmer's pocket.

A dedicated farming assistant that answers crop questions, diagnoses pests and diseases from photos and reports data to the Tamouz platform.

Industry
Agriculture
Status
Completed
Timeline
1403 (SH)
Tamouz Agriculture Chatbot
ASK
GROW BETTER
01 / The Problem

Expert advice rarely reaches the farm in time.

Farmers often face pests, diseases and soil questions without quick access to experts. Delayed diagnosis means lost yield.

Limited access to agronomists
Late pest and disease diagnosis
Generic advice that doesn't fit the farm
Manual, incomplete reporting
Problem image
Photo to diagnosis
in seconds
02 / Our Solution

A personal assistant for every farmer.

Farmers chat in their own language, upload photos of affected plants and receive diagnosis and best-practice guidance. The chatbot also works as an automated reporting agent for the Tamouz platform.

Technical details
Crop Q&A
Crop Q&A
Real-time answers about land and crops
Image Diagnosis
Image Diagnosis
Pest and disease identification from photos
Reporting Agent
Reporting Agent
Seamless data collection for Tamouz
03 / System Architecture

From question to guidance.

Built on language and vision models connected to the Tamouz platform.

Chat & Photos
(Farmer input)
Vision Model
(Disease detection)
LLM
(Expert guidance)
Tamouz
(Data & reports)
04 / Technical Details

What the assistant does.

This dedicated agriculture chatbot empowers farmers with real-time support for their farming needs, streamlining communication and enhancing productivity.

For farmers
  • Ask questions about land and crops
  • Upload images to identify pests or diseases
  • Receive expert guidance on best practices
  • Advanced image support for diagnosing issues directly from photos
  • Personalized agriculture assistant
Plant disease diagnosis
  • Farmers submit both a text description and crop images
  • A vision-language model (VLM) processes these multimodal inputs together
  • Predicts the disease type, covering fungal, bacterial and viral infections, pest damage and nutrient deficiencies
  • Recommends tailored management practices: chemical, biological or cultural
For the Tamouz platform
  • Automated reporting agent
  • Seamless data collection and reporting
Tamouz beyond chat
  • Field maps showing where to apply more or less water and fertiliser
  • Disease diagnosis from a photo, with treatment and prevention advice
  • Weather built for farmers, including harvest-day and overnight frost warnings
05 / Results

Results.

Verified results will be added here.

TBD
Figures to be added
Amirreza Zibafar

The farmer sends a photo of the sick plant, and the AI works out the disease and suggests how to deal with it.

Amirreza Zibafar
Machine Learning Engineer, ZLab
PEOPLE
TECHNOLOGY
COLLABORATION
REAL IMPACT
06 / The Team

The people behind it.

A multidisciplinary team of engineers, researchers and domain experts.

Amirreza Zibafar
Amirreza Zibafar
Machine Learning Engineer
Seyed Ali Ahmadi
Seyed Ali Ahmadi
Tamouz Product Manager
Amirhossein Kakavand
Amirhossein Kakavand
Frontend Development
07 / Gallery

More from this project.

Visuals of the system and its components.

Symptom form and diagnosis result
Disease diagnosis from a photo
Soil nutrient advice
08 / What's Next

Scaling for broader impact.

We are adding more crops, regional knowledge and integration with field sensors.

Discuss a similar project
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