# 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
In the agrifood sector, GANs are used to address challenges related to data scarcity, particularly in scenarios where the collection of real-world data is either costly or limited, especially at large scales. For instance, GANs are used to simulate new climate conditions or pest infestation scenarios, generating additional training data to enhance the robustness of predictive models. These synthetic datasets facilitate more accurate forecasting and decision-making in large-scale agricultural operations, where environmental variability significantly influences crop yields and resource management. Recent studies on Generative Adversarial Networks (GANs) showcase their transformative potential in agriculture and related fields. For instance, [PERSON] et al. [78] utilized GANs to enhance the prediction accuracy of starch content in kudzu through advanced image synthesis, enabling non-destructive quality assessment in food production. Similarly, [PERSON] et al. [79] applied shape and style GANs to generate multispectral data for crop protection. As a result, they provided an innovative data augmentation approach that supports more accurate pest and disease control and monitoring. In the realm of plant disease diagnostics, [PERSON] et al. [80] developed a GAN-based augmentation model to improve leaf disease classification, facilitating faster and more reliable crop management. [PERSON] et al. [81] explored the application of Generative Adversarial Networks (GANs) for generating synthetic agricultural data. Their study evaluated the effectiveness of GAN-based synthetic data for improving the accuracy of predictive models in agricultural monitoring tasks. Conducted in Spain, the research highlighted how GANs could supplement real-world datasets, reducing data scarcity issues common in agricultural research. By generating realistic synthetic data, their approach supports enhanced model training, improving forecasting accuracy in agricultural management. These examples illustrate GANs efficacy in generating high-quality synthetic data, significantly advancing image-based analysis and monitoring capabilities across agricultural applications. Table 1 offers a synthesized view of the methods explored in Section 2, emphasizing their specific contributions to the agrifood industry while facilitating easier reference for readers. The bibliography references in the table correspond to the numbered citations in the References Section of this paper. \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 Supervised Learning & \begin{tabular}{c} Predictive modeling from \\ labeled datasets \\ \end{tabular} & \begin{tabular}{c} Pest detection, yield \\ prediction, product grading \\ \end{tabular} & [2,3,5,6] \\ \hline Unsupervised Learning & \begin{tabular}{c} Pattern recognition in \\ unlabeled datasets \\ \end{tabular} & \begin{tabular}{c} Field segmentation, anomaly \\ detection, soil classification \\ \end{tabular} & [7,10,13] \\ \hline SVMs & \begin{tabular}{c} Classification and regression \\ using hyperplanes \\ \end{tabular} & \begin{tabular}{c} Crop disease detection, \\ vineyard monitoring \\ \end{tabular} & [16, 17, 18] \\ \hline Genetic Algorithms & \begin{tabular}{c} Optimization through \\ evolutionary processes \\ \end{tabular} & \begin{tabular}{c} Production scheduling, \\ irrigation optimization \\ \end{tabular} & [22, 23, 24] \\ \hline Convolutional Neural & \begin{tabular}{c} Feature extraction from \\ networks \\ \end{tabular} & \
\begin{tabular}{c} Crop health monitoring, \\ grid-structured data \\ \end{tabular} &
\begin{tabular}{c} [25, 26, 27] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of AI algorithms and their specific applications in the agrifood industry based on bibliometric analysis.