# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 4 Results
### Analysis of Article Volume, Origin, and Access Model
#### 4.1.10 Hierarchical Cluster Analysis (HCA)  
Using HCA, the analysis revealed a total of seven distinct clusters (Figure 7) among these articles, with each cluster representing a subgroup of research focus within the agrifood sector. To represent the data in a hierarchical component analysis (HCA) scatter plot instead of a heatmap, dimensionality reduction was applied to the similarity data, enabling cluster visualization in a 2D space. This approach is particularly useful for visually analyzing the relationship between articles based on their reference similarities and cluster associations:  
* Cluster 0: This cluster contains articles primarily focused on land-use change, climate impacts, and agricultural sustainability. Many of the articles in this group heavily cite works related to environmental management and food security, reflecting a strong interest in understanding the broader impacts of agricultural practices on ecosystems.
* Cluster 1: Articles in this cluster tend to focus on machine learning applications and AI-driven innovations in precision agriculture. The common references suggest a focus on data analytics, remote sensing technologies, and the development of predictive models for improving crop yields and resource efficiency.
* Cluster 2: This group of articles is centered around soil science and crop management techniques. References commonly include works related to soil properties, irrigation strategies, and yield optimization, indicating a concentration of research on improving soil health and maximizing agricultural output.
* Cluster 3: Articles in this cluster are heavily focused on plant disease detection and pest management. The shared references point to common methodologies for monitoring crop health and preventing disease outbreaks, a critical aspect of sustainable agriculture.  
Figure 6: Similarity heatmap representing research overlaps between key scientific topics.  
* Cluster 4: This cluster covers a range of articles dealing with food quality, safety, and supply chain management. The references shared among these works often relate to methods for enhancing food processing techniques, ensuring safety standards, and improving the efficiency of agricultural supply chains.
* Cluster 5: Articles in this group frequently cite research on deep learning and neural network applications in agriculture. The cluster emphasizes cutting-edge AI technologies, such as image recognition and autonomous systems, applied to precision farming and resource optimization.
* Cluster 6: This final cluster includes articles focused on the socio-economic impacts of agricultural practices and the role of policy in shaping sustainable farming initiatives. The shared references highlight research that bridges technological advancements with economic and social considerations in the agrifood sector.  
These clusters illustrate the diversity of influential research topics within the agrifood domain, providing an understanding of the key methodologies and trends driving innovation. This clustering also underscores the presence of shared knowledge and common reference points within specific subfields, such as precision agriculture, sustainability, and plant health monitoring, contributing to the advancement of agricultural science.