Research is the foundation of many existing policies, practises and procedures within land and crop monitoring, animal health and disease analytics. Numerous methods and procedures are critical to exploring new techniques and thought leading innovations which could potentially lead to ground-breaking discoveries that shift our perceptions, improving agricultural monitoring and management.
Identifying early risk indicators using sensor intelligence, and field analytics can help to detect and predict conditions that increase chance of disease. This can help to reduce economic strain and frustration by identifying optimal spraying times, fertilisations, and slurry distributions to maximise efficiency and sustainability.
Monitoring animal health is critical for producers to ensure good welfare is maintained which ultimately, effects performance and production efficiencies. With remote surveillance including sensor technology, we can accurately and remotely monitor animal movements and behaviours to continuously assess animal health to improve production efficiencies, enhance performance and increase product yield.
Disease causes major disruption to any producer, impacting product yield and performance. Through applied intelligence and analytics of data, we can identify potential targets as risk of disease and make the necessary adjustments to reduce the chance of disease and initiate appropriate intervention post onset of disease.
Using feature extraction, classification and analysis, we can identify disease associated biomarkers to gain insight into possible factors associated and correlated with disease. Leading to timely diagnosis and treatment intervention and enhancing our knowledge of the human body.
The environment is constantly causing producers to face unpredictable challenges which, without notice, can cause major disruption to harvest productions and yields. By automating the process of environment surveillance, we can accurately monitor conditions in real-time to help producers make informed decisions on management programmes across multiple different environments.
Using machine learning and artificial learning, we can constantly expand and deepen our knowledge of current policies and regulations supported by current and thought-leading science. This enhances our ability to make informed choices to progress sustainability, animal welfare and production regulations that benefit farmers, researchers and consumers