Researcher sat in a laboratory using a Apollo to run machine learning models and algorithms to tackle big data analytics
Identifying and managing disease in crops is vital to ensure the safeguarding of future harvests. With sensor intelligence and analytics, researchers can assess and investigate varying nutrient availabilities and the surrounding environment factors to identify threshold levels that increase risk of disease.

Optimised Growing Conditions

Sensor technology alongside AI allows researchers to monitor and explore effects of different environmental levels to reduce risk of disease.

Improved Resource Use

With data analytics and constant surveillance of crops, we can maximise resource use and improve overall costs effectiveness.

Fast Disease Detection

With sensor deployment and drone technology, researchers can quickly identify biomarkers and environmental levels indicative of disease.

Targeted Action

Identification of diseased crops increases abilities of targeted action on specific crops to reduce blanket treatment and increase intervention efficacy.

Improved Pesticide Research

Fast identification of onset of disease and potential biomarkers can be targeted to ensure efficient pesticide use.

Controlled Environments

Disease analytics with sensor technology and applied AI can aid researchers at pinpointing optimal growing levels and threshold boundaries that may increase chance of disease.

Environmental Sensor Analytics

Real-time data can be analysed with machine learning to explore collated data further to identify potential patterns linked to disease.

Industry Leading Challenges

Disease is an ever-growing threat to the economy and food security. True AI can facilitate in feature extraction to identify novel targets for drug applications.

Improved Production Yields

Enhanced disease detection with both sensor and drone technology results in improved production efficiencies and consequently yields.

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