AgBusinesses work on wafer thin margins in variable and complex operating environments. Key to profitability is efficient use of input resources whilst simultaneously maintaining crop quality and maximising yields. Scouting methods are resource intensive, time consuming and inefficient. Huge losses can occur when problematic areas of pest infection or disease is missed, just as too much or too little fertilising can result in large losses over time.

digitalAg’s remote sensing drones are mounted with state-of-the-are Ag sensors to offer an efficient and effective way to survey your AgBusiness operations and assess your crop’s health and yield potential. digitalAg gives you the platform to analyse crop imagery and automatically identify problem areas. Through sophisticated analysis techniques, digitalAg offers targeted remediation actions ensuring the most efficient application of input resource such as water, fertiliser and pesticide thereby ensuring crop health and quality, loss avoidance and yield security.



We specialise in applying the latest digital technologies such as; the cloud, big data and predictive analytics, artificial intelligence and machine learning, geospatial imaging, drones / UAV’s and cryptocurrency / bitcoin, to help AgBusiness achieve crop loss avoidance, quality assurance and yield security

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Case Studies

A look at some case studies where we’ve assisted AgBusinesses identify problem areas and defined remediation steps to ensure crop loss is minimised, quality is maintained and yield is maximised…

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AgTech Articles

Keep updated with the latest Agriculture Technology (AgTech) to assist your AgBusiness avoid crop loss, ensure its quality and maximise yield… 

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digitalAg gives you the platform to manage your crop by avoiding input cost, ensuring crop health and quality, and securing maximum yield.


Leading Technology

digitalAg uses the latest digital technology including Drones, Multi-Spectral Sensors, IoT, Big Data, Predictive Analytics, bitcoin - distributed ledger (Supply Chain Traceability and Verification), Machine Learning / Artificial Intelligence to model crop health, quality and predict yield.