We have developed, integrated, and worked with a number of different camera platforms to feed our machine learning algorithms and produce crop insights.
We have built and supported numerous custom solutions which incorporate high resolution machine vision cameras, including those equipped with integrated GPUs for on-board AI. We have experience in the incorporation of stereoscopic depth maps and fast-focusing lenses for rapid and robust image capture, which facilitates accurate and timely detection of issues in crops.
Our custom software stack has been built to support integration with a wide range of hardware, which itself has been deployed on a diverse set of vehicles including self-propelled sprayers, tractors, and drones.
Our systems have been successfully used for the real-time detection of small and subtle anomalies in crops, including (but not limited to) the Colorado Potato Beetle in potatoes and various diseases and damage in wine grapes.
Our custom software stack has been built to support integration with a wide range of hardware, which itself has been deployed on a diverse set of vehicles including self-propelled sprayers, tractors, and drones.
Our systems have been successfully used for the real-time detection of small and subtle anomalies in crops, including (but not limited to) the Colorado Potato Beetle in potatoes and various diseases and damage in wine grapes.
Real-time detection of pest and disease unlocks the future of treating fields based on actual conditions. Targeted spraying and dose modulation will bring profitability to sustainable farming in a way that contains the outbreak of threats while bringing an understanding of disease propagation patterns, insect life cycle stages and the efficacy of the inputs we are using to control them.
By detecting plant species, herbicide applications can be targeted down to the plant level. Reduce your chemical usage by scheduling applications based on measured weed pressure and targeted spraying through real-time detection.
Know what to expect while requiring fewer eyes on the ground. With low-level insights informed by in-field imagery you can plan your harvest efficiently, thereby reducing waste and maximising profits.
Influence in-field management practices to ensure specific crop quality targets are achieved and make competitive production planning decisions based on early insights into crop quality.