# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 4 Results
### Analysis of Article Volume, Origin, and Access Model
#### 4.1.6 Latent Topics  
Latent Dirichlet Allocation (LDA), a widely used technique in natural language processing for topic modeling, was applied to uncover thematic structures within the corpus of agrifood research articles. This method facilitated the identification of primary areas of focus based on the abstracts, represented by the most frequent and pertinent terms associated with each topic. Five key topics were identified, each characterized by a distinct set of representative words, providing insight into specific areas of research emphasis within the field.  
To conduct this analysis, the abstracts were processed using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to highlight the most significant words. LDA was subsequently applied to detect the underlying topics, with the model configured to identify five distinct themes. The top ten most representative words for each theme were selected to maintain clarity and facilitate interpretation. Abstracts lacking substantial content were excluded to ensure the robustness of the findings.  
The analysis yielded the following five topics:  
Figure 4: Keyword co-occurrence matrix mapping AI algorithm applications in the agrifood industry.  
* Topic 1 is characterized by terms such as 'data','model', 'waste', 'based', 'proposed', 'energy', and 'location', establishing a focus on sustainability and resource management within the agrifood sector.
* Topic 2 features terms such as 'food','microbial','species', 'bacterial','milk', 'products', and 'pathogen', indicating a concentration on microbiology and food safety research.
* Topic 3 revolves around'soil','model', 'water','models', 'yield', 'crop', and 'climate', reflecting an emphasis on soil management, crop yield prediction, and environmental modeling.
* Topic 4 includes words such as 'disease', 'detection', 'plant', 'classification', 'pathogen', and 'control', highlighting a focus on plant disease detection and the application of machine learning in this domain.
* Topic 5 centers on terms such as 'water', 'groundwater', 'carbon', 'economic', 'emission', and 'ecosystem', indicating a focus on environmental impacts, particularly related to water management and carbon emissions in agriculture.  
These topics not only illustrate the diversity of research areas within the agrifood industry but also reveal the deeper intersections between environmental sustainability, biological science, and technological innovations. For instance, both Topic 1 and Topic 5 emphasize a growing research interest in sustainable practices, resource efficiency, and environmental protection. This signals an increasing focus on how AI technologies can aid in addressing climate-related challenges in agriculture. Similarly, the prominence of Topic 2 and Topic 4 suggests a strong focus on food safety and plant disease detection, areas critical to maintaining global food security. The identification of these latent topics underscores the broad range of applications where AI and technological advancements are being leveraged to address both practical and strategic challenges within the agrifood sector. This analysis not only reveals current research priorities but also highlights emerging areas of innovation and potential gaps that future studies may seek to explore.