Data-driven Agriculture Innovation
From predicting weather patterns to optimising arable land usage, the agriculture industry is adopting analytics practices to drive better business and ecological outcomes and ensure we have a continued supply of high-quality food to support the global population.
As the impact of climate change and population growth continues to impart pressure on vital primary industries to produce at scale, industry leaders must strive towards sustainability from both an ecological and economic perspective at the same time. At its core, the agriculture industry must adopt strategies that optimise the consumption of limited resources and minimise the cost of production without costing the earth.
Data science is powering innovation in agriculture across a vast range of use cases, allowing farmers, industry bodies, policy advisers and academic researchers to make decisions and guide industry development towards sustainability and efficiency.
AI and ML techniques are being applied across a huge range agricultural and primary industry applications from optimising plant and equipment utilization to forecasting crop yields, pest and disease prediction, and beyond. Thanks to cloud-enabled data science platforms like AutoStat®, leaders and innovators in farming and primary industry can access the most advanced quantitative techniques to maximise investments and people power for optimal production.
AutoStat® in Agriculture
AutoStat® is currently being used by the Murdoch University livestock research team, where the image analytics module analyses CT scans of sheep carcasses to identify pedigrees that produce the highest quality meat for export. Thanks to code-free machine learning, AutoStat® is enabling Murdoch University use image analytics that ultimately will assist the livestock industry to maximize competitiveness and support an export industry heavily reliant on maintaining a reputation for quality.
In addition to image analytics, AutoStat® enables agriculture organisations and businesses to:
1. Analyse and predict crop yields based with code-free statistical and ML frameworks.
2. Enable private farming businesses to optimize disease and pest intervention
3. Empower the integration of AgriTech solutions into existing infrastructure, integrating real-time data generated from disparate sources (IOT devices, drone telemetry, plant and equipment data, weather data, satellite imagery).
4. Model future demand for agricultural products and commodities by changing key variables and undergoing scenario analysis