# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 2 Artificial Intelligence: An Overview (Fundamentals and Applications in Agriculture)
### Definition and Basics of AI  
AI refers to the ability of computer systems to perform tasks that generally require human cognitive functions, including learning, reasoning, perception, prediction, and decision-making. Essentially, AI involves the creation of algorithms that enable machinesto replicate human cognitive processes, such as dynamically processing and adapting to information over time [17].  
The field of AI is comprised of a multitude of sub-domains (Figure 1):  
**Machine Learning (ML)**: ML algorithms are designed to learn from and make predictions based on data. Techniques such as supervised learning, unsupervised learning, and reinforcement learning fall under this category. In supervised learning, models are trained on labeled data, enabling them to make predictions or classifications. Unsupervised learning involves finding hidden patterns or intrinsic structures in input data without labeled responses. Reinforcement learning trains algorithms through trial and error, using feedback from their actions to learn optimal behaviors [18]. Deep learning, a subset of ML, uses multi-layered neural networks to model complex patterns in data. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are key architectures within deep learning, used for image and sequence data respectively [19].  
**Computer Vision**: This field enables machines to interpret and make decisions based on visual data. CNNs have been pivotal in advancing image recognition capabilities. Techniques in computer vision include object detection, image segmentation, and facial recognition, which are widely used in applications ranging from autonomous vehicles to security systems [8]. Advances in hardware, such as Graphics Processing Units (GPUs), have significantly enhanced the processing power available for computer vision tasks, allowing for real-time analysis and decision-making [20].  
**Natural Language Processing (NLP)**: NLP allows machines to process and generate human language. Techniques in NLP include syntactic parsing, sentiment analysis, and machine translation. The development of transformers and attention mechanisms has significantly improved the performance of NLP models, enabling them to handle tasks such as text generation, question answering, and language translation with high accuracy [21]. NLP applications are found in virtual assistants, chatbots, and automated customer service, transforming how humans interact with machines [22].  
**Expert Systems**: These represent human knowledge in structured domains. They use a set of rules to analyze information and make decisions or solve problems in specific areas. Expert systems are particularly useful in fields requiring specialized knowledge, such as medical diagnosis and financial forecasting. Recent advancements have demonstrated their efficacy in these applications, particularly in enhancing decision-making processes and improving diagnostic accuracy [23].  
**Robotics**: Integrating AI with robotics has led to the development of autonomous systems capable of performing complex tasks in dynamic environments. Robotics combines mechanical engineering, electrical engineering, and computer science to design and build robots. AI enhances robotics through the implementation of advanced algorithms for path planning, object manipulation, and human-robot interaction. Examples of AI-driven robotics applications that improve efficiency and safety include autonomous drones, robotic arms in manufacturing, and service robots in healthcare [24].