GRAVITY 06 Solutions & Awards

Cropin Cloud integrates state-of-the-art technologies like AI, machine learning, and satellite imagery to enhance pest and disease management in cotton farming across Northern Australia. Featuring a 14-day forecasting capability for early alerts, the platform utilizes finely calibrated AI models for accurate threat detection and includes specialized models to assess nitrogen uptake and water stress. Designed to facilitate seamless communication among stakeholders and prioritize data security, Cropin Cloud aims to equip farmers with the tools for proactive management, thereby bolstering biosecurity and enhancing crop health through real-time insights.

D-CAT’s multi-faceted solution will utilise satellite remote sensing, open-source data, AI/machine learning, data fusion, and the latest processing technologies to help transform Telstra InfraCo’s infrastructure management. The solution will deliver new insights, more timely data and trusted forecasts across its full network, with business savings and efficiencies resulting from the novel appoach, as well as the potential for new operational practices and continued improvements that will keep track with ever-improving source data.

Asset360 for Comprehensive Asset Monitoring leverages cutting-edge spatial and terrestrial technologies, our state-of-the-art Asset360 platform delivers unparalleled, holistic surveillance and management of essential assets for superior oversight and control.

Spottitt MF focuses on leveraging AI technology and earth observation satellites to address the challenge of managing over 13,000 km of power transmission easements efficiently. The solution includes four key components: AI-based generation of an inventory of access tracks, periodic monitoring of extreme weather conditions, real-time updates on track conditions, and feedback integration for continuous improvement.

SatSense proposed satellite InSAR for Transport Network Monitoring. Satellite InSAR provides a solution whereby the whole network can be analysed for historical movement, and continuously monitored to provide updates when ground movement patterns change. Their solution will use Machine Learning approaches to highlight areas of the road and rail network at risk from ground movement; allowing engineers and asset managers to prioritize maintenance efforts, optimally deploy ground equipment, and reduce the need for boots-on-the-ground inspections.

Big Terra & ALMOSTO proposed a comprehensive approach that addresses sustainability concerns and enhances operational efficiency. By integrating satellite imagery, machine learning technology, and expert advice, their platform provides vineyard managers, cooperatives, and associations with the tools they need to navigate the transition to regenerative viticulture effectively. Furthermore, our platform ensures compliance with relevant regulatory frameworks through transparent data collection and reporting mechanisms. By providing a centralized platform for data management and analysis, we empower vineyards to meet certification requirements and regulatory obligations.

CropIn Cloud incorporates advanced machine learning models for pest and disease insights (DEWS), crop health monitoring, yield prediction with weather data integration, and comprehensive farm management through a unified dashboard. It aims to enhance vineyard efficiency and sustainability by providing plot-level intelligence, advisory services, and a flexible system for integrating various data sources. Tailored to meet Bolney's specific challenges amid climate variability, CropIn's Cloud leverages analytics and predictive modelling, equipping the estate with actionable insights to refine operations and enhance both environmental and economic sustainability.

Deep Planet proposed VineSignal: a solution which combines satellite imagery, ground sensor and agronomy data, and machine learning algorithms to remotely monitor vineyards and detect early signs and risk of disease. This scalable solution builds on Deep Planet’s existing disease detection model with data from Bolney vineyards to generate disease probability maps. In addition, Deep Planet's VineSignal platform provides insights to understand the variability in soil nutrients (NPK) and organic carbon, detect anomalies, monitor soil moisture and evapotranspiration.

Deep Planet has pioneered a powerful machine learning and analytics platform specifically designed for regenerative agriculture. This innovative solution leverages Planet's extensive satellite dataset, along with Deep Planet's regenerative agriculture metrics to offer a comprehensive suite of features to empower farmers and stakeholders in the agricultural industry.