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Use CaseManufacturing & Industrials / Agriculture

Optimise agricultural production process often in real time

Optimise farming production process, often in real time, determining where to dedicate resources to reduce bottlenecks, cycle time and error rates. Interventions might range from automated fertiliser or pesticide deployment to alerting for human involvment.

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Revenue

Revenue - Product optimisation

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Operational Support

Operational Support - Issue triage

£

Cost

Cost - Reduce wastage

Description

Optimise agricultural production process often in real time

Benefits & ROI

0.5

Linked Case Studies

Makoto Cucumber Farm

Makoto Cucumber Farm automatically sorts cucumbers based on shape, length and distortion using deep machine vision

"NITI Aayog (National Institution for Transforming India), Government of India"

The National Institution for Transforming India partners with IBM to develop a crop yield prediction model using AI

ecoRobotix aims to reduce the amount of herbicide farmers use by 20 times with robotics and machine vision

Hart Agriculture’s Seven Oaks Dairy farm

Farmers are able to track cows and monitor their activity and optimise food production through machine learning

Small Robot Company

Small Robot Company is a vendor which hopes to deploy robots to reduce chemicals and emissions while increasing farm profitability

ICRISAT and Microsoft advise farmers on when to sow crops resulting in a 10-30% yield per hectare increase by using machine learning

United Phosphorous (UPL)

United Phosphorous (UPL) and Microsoft machine learning predictions were used to alert farmers to the likelihood of pest infestation resulting in the doubling of farm income

PEAT identifies 400 diseases and pests from farmers'' uploaded photos on their new mobile app

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Source: mckinsey.com · Editor: original-sdg