# 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
By optimizing evapotranspiration models, the study provided a scalable framework for managing water resources in regions prone to water scarcity, demonstrating the algorithm's capacity to enhance agricultural sustainability through precise environmental monitoring. These studies underscore PSO's effectiveness in refining operational efficiency and adapting to diverse real-world challenges. Autoregressive Time Series Models (ARIMA): A Class of Time Series Models for Analyzing and Forecasting Temporal Patterns  
ARIMA models are a class of time-series forecasting techniques that combine autoregression, differencing, and moving averages to analyze data points that change over time. ARIMA models are particularly effective for handling data with trends or seasonality, making them suitable for a wide range of forecasting applications. These models rely on historical data to make predictions about future values, accounting for both temporal dependencies and random fluctuations. The detailed methodology and theoretical foundation of ARIMA models are comprehensively discussed in \"Time Series Analysis: Forecasting and Control\" [66]. In the agrifood sector, ARIMA models have several valuable applications. They are commonly used to forecast weather patterns, which are critical for agricultural planning and risk management. Additionally, ARIMA models can predict product demand, enabling more efficient supply chain management and reducing waste. Crop yield forecasting across different seasons is another key application, allowing for better resource allocation and strategic decision-making in planting and harvesting. By providing accurate predictions of such essential variables, ARIMA models support long term planning and improve operational efficiency in agriculture. For example, ARIMA models have been effectively utilized for forecasting crop growth parameters [67] employing ARIMA models to predict canopy cover in cotton fields. Recent applications of Autoregressive Time Series Models, particularly the ARIMA model, highlight their effectiveness in forecasting complex environmental and economic patterns. For example, [PERSON] and [PERSON] [68] utilize ARIMA to model drought occurrences in semi-arid regions, providing insights into rainfall variability and improving water resource management. In the energy sector, [PERSON] et al. [69] employ ARIMA models to forecast energy demand trends based on historical consumption data, aiding in resource allocation and policy planning for sustainable development. Additionally, [PERSON] et al. [70] apply ARIMA within precision agriculture, using it for accurate weather predictions that optimize planting and harvesting schedules. These studies underscore ARIMA's adaptability in handling time-series data, offering robust predictive capabilities for varied applications in environmental management and resource planning. Hierarchical Clustering Algorithms: A Class of Unsupervised Learning Methods for Creating Nested Clusters Based on Data Similarity  
This method of clustering builds a hierarchy of nested clusters by either merging smaller clusters into larger ones (agglomerative) or splitting larger clusters into smaller ones (divisive). This approach allows for a flexible grouping structure, where clusters can be examined at different levels of granularity. Hierarchical clustering does not require a predefined number of clusters, which makes it particularly useful when the number of distinct groups in the data is unknown. The detailed methods and applications of hierarchical clustering are thoroughly explained in \"Data Mining: Concepts and Techniques\" [71]. In agriculture, hierarchical clustering is used to structure data pertaining to crops, soil properties, and climate conditions. For instance, this technique is applied to categorize regions based on similarities in soil composition, thereby facilitating more targeted and efficient land-use planning.