Farm management is centered around regulation compliance and it is vital that businesses can adapt to changes in policy. Applied machine learning can help to address key regulation points such as animal welfare, land and environmental management to ensure complete farm compliance.
To remain productive and economically viable, it is vital that farm businesses are able to acclimatise to changes in policy. Sensor deployment and farm management tools offered through machine learning platforms enable efficient solutions to the latest regulation.
Successful farm management is the key to a productive business and is guided by agricultural policy and regulation. Deep learning algorithms can provide producers with the solutions to effective management for livestock, arable and mixed farming.
Artificial intelligence enables farmers to constantly monitor livestock health to improve animal welfare, while sensors can monitor agricultural pollution in real-time to ensure full regulation compliance and easier business management.
Animal health and welfare are directly linked to performance while both regulation and consumer demands support rising standards, therefore, it is imperative that livestock farms keep this at the forefront of management. This is easily achieved through real-time monitoring and the intelligent interpretation of data with machine learning analytics.
Changes in policy have shifted the focus away from intensive production towards enhanced environmental management. Adapting to these changes can be achieved through intelligent sensor deployment that can monitor and analyse land and environmental conditions in real-time.
Address animal welfare, land and environmental management with artificial intelligence and machine learning platforms that provide enhanced farm business management tools to effectively comply with regulation while going above and beyond.
Sensors can be placed directly in fields and livestock buildings to monitor conditions, allowing the effortless maintenance of optimal environments. Water quality can be assessed through in-situ sensors in agricultural watercourses while soil health can be analysed using sensors placed directly in the ground.
Scientific research has formed the basis for policy and regulation, influencing changes and determining the way in which agricultural systems operate. Applied machine learning enhances our understanding of the farming sector and can contribute to encouraging researcher and farmer collaboration in policy.