Operationalizing digital twins in agriculture with machine learning is an exciting prospect for farmers. For a long time, decision support systems have relied on static models, hindering progress towards a more advanced platform with automation, not tailor-made for specific tasks. This has resulted in a significant loss of valuable insights that can be extracted from data produced by the agricultural system. However, digital twins are becoming increasingly popular in other disciplines and offer unique benefits that have yet to be fully realized in agriculture.
Despite the success seen in other sectors, there is a lack of awareness and understanding of the potential of digital twins in agriculture. This thesis aims to investigate the operationalization of digital twins with machine learning, starting with their ability to make predictions in situations when the data is insufficient or lacks temporal resolution. The research also explores the possibility of transferring digital twins to diverse conditions, enabling them to offer valuable insights to farmers regardless of where they operate.
One of the biggest challenges that farmers face is the unavailability of sufficient data to make informed decisions. Digital twins can help overcome this obstacle by replicating real-world conditions and situations. The ability to simulate real-world data enables farmers to make accurate predictions and informed decisions based on incomplete data.
Digital twins can also learn and adapt to diverse conditions, offering valuable insights to farmers regardless of where they operate. This is particularly important in agriculture, where the conditions can vary significantly from one location to another. The adaptability of digital twins will ensure that farmers have access to valuable insights that can help them make informed decisions about their crop and livestock.
In conclusion, the operationalization of digital twins in agriculture with machine learning is a promising platform for farmers. The ability to make predictions even with insufficient or incomplete data and the adaptability to diverse conditions is vital for modern agricultural systems to thrive. Despite the success seen in other fields, the potential of digital twins in agriculture is yet to be fully realized. This thesis aims to bridge this gap and unlock the full potential of digital twins in agriculture.
