# 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 extracting pertinent information, NLP facilitates more informed decision-making processes that can significantly influence agricultural practices, supply chain optimization, and food security management. The integration of NLP into AI-driven systems offers considerable advantages, including enhanced predictive capabilities for food pricing, climate variability, and pest detection. Recent scholarly investigations into NLP algorithms demonstrate their extensive applicability across diverse fields. For instance, [PERSON] et al. [51] developed an NLP-based system designed to analyze the composition of life sciences articles, thereby automating the extraction of biological and chemical information from unstructured text. This methodology enhances data accessibility in biomedical research by converting extensive textual information into structured datasets. Similarly, [PERSON] et al. [52] developed an NLP-based system designed to analyze the composition of life sciences articles, thereby automating the extraction of biological and chemical information from unstructured text. This methodology enhances data accessibility in biomedical research by converting extensive textual information into structured datasets. In the context of Spain, [PERSON] et al. [53] determined that NLP, particularly through sentiment analysis of social media posts, serves as an instrumental tool for evaluating agricultural and rural policies, such as the European Common Agricultural Policy (CAP). Their study elucidates how the analysis of public sentiment on platforms such as Twitter (currently X) uncovers citizen concerns and priorities in areas including environmental policies, food safety, and funding allocation. The findings suggest that incorporating NLP into the policymaking process effectively complements traditional evaluation methods, facilitating a more responsive approach to public opinion and improving policy alignment with societal needs. Furthermore, [PERSON] [54] explored the application of NLP in linguistic analysis, specifically in translating descriptors within the agifood sector. This application supports the internationalization of Spanish food products by generating culturally accurate translations, thereby enhancing market accessibility and competitiveness. Fuzzy Logic: A Mathematical Approach for Handling Uncertainty and Imprecision in Decision-Making Systems  
Fuzzy logic provides a robust and versatile framework for decision-making in environments marked by uncertainty and imprecision, rendering it exceptionally suitable for applications where traditional binary logic falls short. In the agricultural domain, fuzzy logic plays a critical role in optimizing systems such as automatic irrigation and fertilization. These systems must navigate uncertain variables, including fluctuating soil moisture levels, varying temperatures, and nutrient concentrations. By facilitating reasoning based on degrees of truth rather than strict binary classifications, fuzzy logic enables these systems to execute more nuanced and adaptive adjustments. Consequently, this enhances resource efficiency and promotes the adoption of improved agricultural practices, particularly within the realm of precision farming. Here, the judicious use of water and fertilizers is paramount for achieving both sustainability and productivity. These foundational concepts are comprehensively examined in \"Fuzzy Logic with Engineering Applications\" [55]. A practical implementation of fuzzy logic is exemplified by the deployment of a Fuzzy Logic Controller (FLC) for managing the microclimate within agricultural greenhouses. This application addresses the complexity introduced by numerous perturbations and a multitude of input parameters that influence the internal climate [56]. Additionally, [PERSON] et al. [57] developed a self-tuning adaptive control system grounded in fuzzy logic for sustainable greenhouse management. Their system dynamically adjusts climate variables, thereby ensuring optimal growth conditions while simultaneously minimizing resource consumption.