Accurate prediction models of disease can help producers to make informed preventative decisions to safeguard current and future crops and stocks.
With sensor technology, researchers can collect real-time data within multiple environments to ascertain vital information on disease factors. With sensors, data is constant and can facilitate in making numerous on farm business decisions.
Disease analytics with artificial intelligence allows researchers to investigate deeper in to current knowledge of disease and facilitate in improving overall animal and crop health.
With machine learning and artificial intelligence, we can go further than ever before, to explore current data and dive further to ascertain vital information on disease factors to help producers make critical decisions.
Disease causes detrimental effects on performance and productivity. Machine learning can aid researchers to explore disease indicators and environmental levels and understand thresholds to reduce the impact of disease.
Threshold levels can be explored further using artificial intelligence to identify predetermining factors indicative of disease and facilitate in creating and executing strategic plans to reduce the likelihood of disease.
Sensor technology can collate vital information on environmental conditions, which when analysed, could prove vital at predicting and most importantly preventing disease.
Field and crop image capture and monitoring can assist in identifying biosecurity risks and detect possible factors increasing spread of disease.
With increased knowledge of disease-causing factors, we can limit use of intervention techniques and assist in reducing resistance.