# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry
### Random Forest: A Supervised Ensemble Learning Algorithm Using Decision Trees
Moreover, hierarchical clustering groups crops according to their growth patterns and environmental requirements, optimizing the allocation of resources such as irrigation and fertilization. By identifying inherent groupings within agricultural data, hierarchical clustering aids in decision-making processes that enhance resource efficiency and improve overall agricultural management strategies. Recent studies on hierarchical clustering algorithms reveal their versatility across diverse scientific disciplines. For instance, [PERSON] et al. [72] applied hierarchical clustering to analyze plant responses under stress conditions, particularly salinity, facilitating the grouping of genetic traits and improving breeding strategies for stress resilience. In a different field, [PERSON] et al. [73] utilized hierarchical clustering in food safety, applying it to detect chemical compounds in food samples. This method enhances detection accuracy by grouping similar chemical profiles, supporting better regulatory practices. Additionally, [PERSON] et al. [74] explored hierarchical clustering to analyze trends in sustainable agricultural technologies. This study allowed researchers to classify and evaluate emerging technologies based on performance metrics. In agricultural applications, hierarchical clustering algorithms have been effectively utilized for crop classification [75], presenting a model that integrates multiple data sources to enhance classification accuracy. The researchers employed a dataset comprising 7.4 million agricultural parcels in France and the Netherlands, associating each parcel with time-series data from Sentinel-2 and Landsat 8 satellite observations, as well as biophysical variables such as Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The model utilizes a hierarchical fusion approach to combine satellite imagery, crop rotation patterns, and local crop distribution data. Performance assessments at different levels of semantic aggregation--ranging from 151 to 8 crop types or groups--yielded accuracy rates between 85% and 95%, depending on the dataset and aggregation level. These results underscore the efficacy of hierarchical clustering algorithms in agricultural applications, highlighting the importance of integrating diverse data sources to achieve high precision in crop classification tasks. A relevant application of hierarchical clustering in a Mediterranean agricultural context is the enhancement of crop yield prediction accuracy, as explored by [PERSON] et al. [76]. Their study, conducted in Italy, applied hierarchical clustering to group agricultural fields based on phenological and environmental data derived from remote sensing sources. By segmenting fields into clusters with similar growth conditions, the researchers improved yield forecasting models, enabling more precise crop management strategies. This demonstrates the potential of hierarchical clustering to optimize agricultural planning through data-driven decision-making. These applications highlight hierarchical clustering's capability to manage complex datasets, offering insights that advance research in environmental resilience, food safety, and technological innovation. Generative Adversarial Networks (GANs): A Class of Deep Learning Architectures for Generating Synthetic Data Through Adversarial Training  
Generative Adversarial Networks (GANs) consist of two neural networks: a generator and a discriminator. These networks engage in a process of competition, where the generator creates synthetic data instances, and the discriminator attempts to distinguish between real and generated data. Through this adversarial process, the generator improves its ability to produce increasingly realistic data. This innovative approach to data genera tion is introduced by [PERSON] et al. in \"Deep Learning\" [77] and has since become a cornerstone in fields requiring synthetic data generation.