# 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
Recent advancements in RL have catalyzed innovative applications within the agricultural sector, offering adaptive solutions that optimize resource management and enhance production efficiency, involving the utilization of RL in crop yield prediction, where real-time environmental data informs cultivation decisions, thereby fostering agricultural practices that exhibit greater resilience to climate variability [47]. These studies underscore RL's potential not only to automate but also to personalize agricultural tasks, such as monitoring and irrigation control, thereby increasing the sustainability of contemporary farming systems and presenting a Deep Recurrent Q-Network (DRQN) model that integrates a Recurrent Neural Network (RNN) with Q-Learning. The model processes sequentially data related to environmental factors, soil conditions, water availability, and crop parameters. The RNN component captures temporal dependencies in the data, while the Q-Learning algorithm constructs a crop yield prediction environment based on the input parameters. A linear layer maps the RNN outputs to Q-values, enabling the reinforcement learning agent to predict crop yields by minimizing error and maximizing forecast accuracy. The model was trained and evaluated on a dataset comprising various agronomic factors, achieving an accuracy of 93.7% in crop yield prediction. Expanding beyond agriculture, recent research on RL demonstrates its versatile applications across a spectrum of fields, including healthcare. For instance, [PERSON] et al. [48] developed an RL-based model that integrates crop data to optimize resource allocation, dynamically adapting to varying agricultural conditions. This model enhances farming decision-making by aligning resource distribution with real-time data inputs. Similarly, [PERSON] et al. [49] explored the application of RL for irrigation control, incorporating deep learning techniques to adjust water usage based on environmental factors. This model effectively reduces water waste, thereby promoting sustainable agricultural practices. In agriculture, [PERSON] et al. [50] demonstrate RL, including multi-armed bandits, as an effective machine learning method for sequential decision-making in uncertain conditions. While RL is widely recognized for successes such as creating the world's best Go player, its application potential spans various fields, including crop management decision support systems (DSS). In these systems, RL offers an interactive, action-oriented, and context-sensitive approach to managing sequential crop operations under uncertainty. A review shows a limited but expanding body of work with promising applications for real-world, human-centered DSS in agriculture, calling for combined efforts from RL and agronomy experts to advance theoretical and practical development in this area. Collectively, these studies illustrate the expansive usefulness of RL in providing tailored, efficient, and sustainable solutions. By enabling both automation and personalization of critical tasks, RL significantly contributes to the advancement of operational efficiencies and the achievement of sustainability goals within diverse industries. Natural Language Processing (NLP): A Subfield of AI Focused on the Interaction Between Computers and Human Language  
In recent years, natural language processing (NLP) has undergone a profound transformation, emerging as one of the most groundbreaking advancements in artificial intelligence (AI). This evolution holds significant implications for its application within the agifood sector. Despite the transformative potential of NLP, its benefits remain underexplored in the existing literature on AI applications in agriculture, as evidenced by the reviewed articles and referenced academic works. NLP's capacity to analyze and interpret vast volumes of unstructured textual data--ranging from weather reports and scientific publications to market trend analyses--introduces an innovative dimension of insight for the agifood industry.