# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 3 Materials and Methods
### Data Retrieval  
A comprehensive search of scholarly articles was conducted utilizing the SCOPUS database. SCOPUS was selected for its extensive coverage of peer-reviewed literature across a broad spectrum of scientific disciplines, thereby ensuring the assembly of a robust and pertinent collection of publications. A meticulously crafted search query was designed to capture a wide array of AI methodologies and their applications within the agrifood sector. This query was formulated to incorporate a diverse range of AI-related terms alongside sector-specific keywords, ensuring the inclusion of both general AI approaches and specialized techniques. The specific search query used is as follows:  
TTILE-ABS-KEY((\"artificial intelligence\" OR \"machine learning\" OR \"deep learning\" OR \"computational intelligence\" OR \"data mining\" OR \"neural networks\" OR \"genetic algorithms\" OR \"support vector machines\" OR \"fuzzy logic\" OR \"reinforcement learning\" OR \"convolutional neural networks\" OR \"random forest\" OR \"natural language processing\" OR \"particle swarm optimization\" OR \"time series analysis\" OR \"hierarchical clustering\" OR \"generative adversarial networks\") AND (algorithm* OR model* OR technique* OR application*) AND (\"agrifood\" OR \"agriculture\" OR \"horticulture\" OR \"farming\" OR \"food industry\" OR \"food processing\" OR \"food \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm/Technique** & **Core Function** & \begin{tabular}{c} **Potential Applications in the** \\ **Agrifood Industry** \\ \end{tabular} & \begin{tabular}{c} **Bibliography** \\ **References** \\ \end{tabular} \\ \hline K-Means Clustering & \begin{tabular}{c} Data partitioning into \\ homogeneous clusters \\ \end{tabular} & Field zoning, yield estimation & [14; 28] \\ \hline Random Forest & \begin{tabular}{c} Ensemble learning with \\ decision trees \\ \end{tabular} & Yield prediction, pest outbreak & [29; 30; 31] \\ \hline Reinforcement Learning & \begin{tabular}{c} Decision-making \\ through interaction \\ \end{tabular} & \begin{tabular}{c} Irrigation management, \\ adaptive resource allocation \\ \end{tabular} & [32; 33; 36] \\ \hline Natural Language Processing & \begin{tabular}{c} Text analysis and \\ language understanding \\ \end{tabular} & \begin{tabular}{c} Market analysis, \\ policy evaluation, \\ sentiment analysis \\ \end{tabular} & [37; 39; 40] \\ \hline Fuzzy Logic & Reasoning under uncertainty & \begin{tabular}{c} Climate control, \\ precision irrigation \\ \end{tabular} & [41; 42; 44] \\ \hline Particle Swarm Optimization & \begin{tabular}{c} Optimization through \\ swarm-based exploration \\ \end{tabular} & \begin{tabular}{c} Resource management, route \\ optimization \\ \end{tabular} & [46; 47; 48] \\ \hline ARIMA Models & Time series forecasting & \begin{tabular}{c} Weather forecasting, demand \\ prediction \\ \end{tabular} & [50; 51; 53] \\ \hline Hierarchical Clustering & Creation of nested data clusters & \begin{tabular}{c} Land-use planning, \\ crop grouping \\ \end{tabular} & [54; 55; 57] \\ \hline Generative Adversarial & \
\begin{tabular}{c} Synthetic data generation \\ through adversarial learning \\ \end{tabular} &
\begin{tabular}{c} Data augmentation, \\ crop monitoring, \\ disease diagnostics \\ \end{tabular} & [58; 59; 60] \\ \hline \hline \end{tabular}
\end{table}