What challenges did Buhler face before AI implementation?

Sustainable farming practices needed for growing populations and climate challenges. Inefficient application of water and fertilizer leads to resource waste. Difficult early detection of crop diseases affects yields. High risk of food contamination by toxins such as aflatoxins in processed food. Infrastructure barriers to digital transformation in rural farming communities.

Source

MicrosoftScaled productionEvidence: Low40/100

Buhler advances food safety and precision farming with AI-driven automation

Use case typeAgriculture optimizationUpdated Jun 13, 2026

Buhler, a global leader in food processing equipment, implemented Microsoft Machine Learning Server and Azure ML to transform agricultural and food processing practices. By automating the detection of crop diseases and food contaminants, and optimizing the application of water and fertilizer, Buhler enables actionable precision agriculture and safer food products. Buhler's LumoVision system, enhanced by Microsoft technologies, uses machine learning and computer vision to identify carcinogenic contaminants in maize, improving food safety outcomes. The solution also powers predictive analytics for online food retailers, enhancing customer experience through automated order prediction. These initiatives represent a holistic transformation, increasing sustainability, efficiency, and safety from farm to table. The case highlights deployments spanning developed and developing economies, leveraging Microsoft's cloud to overcome connectivity barriers in rural areas. Buhler's use of AI technologies demonstrates significant advances in agricultural productivity, resource usage, and public health.

Organization: Buhler
Industry: Agriculture
Location: Global
Published: April 2021

Reported outcomes

Strategic outcomes

New product / capabilityEnabled precision farming optimizationRisk & complianceImproved food safety by removing contaminantsMarket & geographic expansionExpanded cloud-based tools to rural communitiesCustomer experience & trustEnhanced online order prediction experience

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Buhler
Provider: Microsoft
Maturity: Scaled Production
Linked source: cognillo.com

Improved food safety by removing toxic contaminants from food at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Precision Agriculture with ML-powered Irrigation and Fertilization
2AI-based Crop Disease Detection
3Automated Food Contaminant Sorting with Computer Vision

Sustainable farming practices needed for growing populations and climate challenges.
Inefficient application of water and fertilizer leads to resource waste.
Difficult early detection of crop diseases affects yields.
High risk of food contamination by toxins such as aflatoxins in processed food.
Infrastructure barriers to digital transformation in rural farming communities.

Implemented Microsoft Machine Learning Server and Azure ML for agricultural data analytics and automation.
Applied AI models to sensor, drone, and camera data for precision irrigation, fertilization, and crop disease detection.
Developed optical sorting system (LumoVision) combining computer vision and machine learning to remove carcinogenic aflatoxins from maize.
Leveraged cloud-based ML for rural communities, aided by Microsoft's Airband internet initiative.
Enabled predictive analytics for food retailers to automate online order prediction.

Technologies

Machine Learning Azure ML

Enabled early detection and prevention of crop disease, improving yield.
Reduced water and fertilizer consumption via precision farming.
Improved food safety by removing toxic contaminants from food at scale.
Empowered rural communities to access cloud-based farming optimization tools.
Enhanced online food retail customer experience with AI-driven order prediction.

Architecture

Sensor, drone, and camera data are continuously gathered from farming and processing operations. Data streams are uploaded to Microsoft Azure cloud services, where Microsoft Machine Learning Server and Azure ML process the information for detection of disease, prediction, and recommendation generation. LumoVision optical sorter uses real-time computer vision and ML models to identify and eject contaminated maize. Microsoft's Airband initiative provides rural internet access, allowing remote farms to interact with ML-powered cloud analytics. Food retailers integrate ML models to predict and automate customer ordering based on behavioral data.

Sources & evidence1

Evidence: Low40/100Evidence strength