Digital farming has the potential to revolutionize agricultural production, leading to both increased crop yields (higher revenue) and more precise application of nutrients, crop protection chemicals and water (lower costs). But how does this work? A crucial element is machine learning (ML) and artificial intelligence (AI). While these topics are widely discussed in the general public, many people still lack a solid understanding of these technologies. The goal of ML and AI is to teach computers how to do what people can do better, and learning is a major priority.
Various types of ML exist, such as pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems and self-organising systems. All of these concepts are used for specific tasks, depending on the context and the problem set. ML is ultimately all about prediction: predicting what someone wants, the results of actions and how the physical world could change in the future. Furthermore, algorithms can be used to analyse data sets and come to conclusions that can be used to make decisions automatically.
At the same time, architecture has to be considered in terms of machine learning and AI. All systems have an architecture, either intended or not. It is our job to understand and manage the architecture while understanding the process of algorithms that are used to predict the outcomes and manage the system. The difficulty lies in combining the various algorithmic concepts in order to solve complex systems. Additionally, quantum computing shows potential, as it provides access to a world with various states rather than the two prescribed by computing. The emergence of quantum computing may lead to a greater understanding of artificial intelligence, the master algorithm or even consciousness.
In addition, much data needs to be processed in order to fully understand the nuances associated with artificial intelligence. Solutions must be found to store and access this data, with advancements in communications and a break down in the enforced standards of relation databases. These four concepts- relational, object, key value, and graph data models- are being explored and implemented to the algorithmic world. It is also important to remember that algorithms are only useful when they can be clearly expressed and implemented in formal structures.
As this field of research progresses, it is believed that we may come close to understanding consciousness itself. But for now, a combination of algorithms must be chosen, depending on the architecture and the problem set. With research into algorithms, architecture and databases, ML and AI in digital farming could revolutionise how we grow and use crops.
