Transforming Agriculture

We help farmers and agribusinesses maximize yields, optimize resources, and build sustainable operations with AI solutions built for Canadian agriculture.

Agriculture Challenges We Solve

Modern agriculture requires intelligent resource management and sustainability

Unpredictable weather and climate variability

Resource optimization (water, fertilizer, labor)

Crop and livestock health monitoring at scale

Sustainability and environmental compliance

Our Agriculture AI Solutions

Edge AI + Agentic AI powering sustainable, efficient farming

Precision Farming

Edge AI + IoT sensors optimizing irrigation, fertilization, and planting decisions based on real-time field data

Smart Irrigation Systems

AI-powered water management reducing waste while maximizing crop yields

Crop & Livestock Monitoring

Computer vision and sensors tracking health, growth patterns, and early disease detection

Sustainable Practices

AI models optimizing resource use, reducing emissions, and tracking carbon footprint

Indigenous Knowledge Integration

Combining traditional knowledge with AI insights for culturally appropriate sustainable farming

Why Farmers Choose M2M

290% Average ROI

Across agricultural deployments

Edge Processing

Real-time AI in remote farm locations

Up to 75% Funding

Government subsidies available

Canadian Climate

Built for diverse Canadian growing conditions

Proven Impact

From Manual Monitoring → To Real-Time AI Insights
From Centralized Data Dependency → To Edge Intelligence
From Generic Solutions → To Configurable, Use-Case Specific Tools
From High-Cost Deployments → To Scalable, Affordable Adoption

+30%

Crop Yield Accuracy

-40%

Water & Energy Use

Faster Decisions

-25%

Reduction in Crop Loss

Case Study

Pathoscan

Accelerating Crop Disease Detection with Pathoscan

1
The Challenge

Pathoscan aimed to improve early detection of crop diseases using their PathoBox assay kits. Traditional lab-based analysis was slow, requiring expert validation and causing delays in intervention during narrow disease windows.

2
Our Approach

We collaborated with Pathoscan to build a scalable cloud ML pipeline that processes image data from biochemical assays. Our team implemented advanced image preprocessing, reaction classification, and developed a highly accurate machine learning model for real-time pathogen detection.

3
Key Results

92%

Accuracy in pathogen detection

<5s

Reduced processing time

91%

AUC score for reliability

4
Impact

The project laid the foundation for Pathoscan's transition to a field-ready, automated detection tool, reducing reliance on manual interpretation and enabling scalable deployment across farms.

Key Outcomes

Metric	Result
Crop Yield Accuracy	+30%
Reduction in Water & Energy Use	40%
Accuracy in Pathogen Detection	92%
Reduction in Crop Loss	25%

Ready to Transform Your Farm?

Let's discuss how Edge + Agentic AI can optimize your operations and increase sustainability.