# Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations
## 5 Conclusions  
This study explores the use of deep learning models for fruit quality assessment, focusing on the comparison between training a model from scratch and using transfer learning with MobileNetV2. The results indicate that while both approaches achieved high accuracy, the transfer learning model demonstrated faster convergence and slightly better performance, particularly in the context of limited data availability.  
The choice of the Fruits-360 dataset and its specific subset was driven by the need to simulate real-world conditions faced by small-scale producers who typically work with limited fruit varieties. By including a second fruit type, such as the golden apple, the objective was to introduce diversity and reduce the risk of overfitting, thereby improving the model's generalization capability.  
MobileNetV2 was selected due to its compact size and efficiency, making it an ideal candidate for deployment in environments with limited computational resources. This aligns with the goal of developing a practical and accessible tool for fruit quality assessment that can be used by small-scale farmers and producers.  
The feature maps provided insights into the model's decision-making process, during which the neurons tended to activate around damaged spots on fruits. This interpretability is crucial for gaining the trust of end users who may have limited technical expertise, thus promoting the broader adoption of AI-based tools in agriculture by enhancing the transparency of the model's decisions.  
However, the rapid convergence to high accuracy also highlighted potential limitations of the dataset, suggesting that more diverse and challenging data may be required to ensure robust performance in real-world scenarios. Future work should focus on expanding the dataset and exploring additional techniques to further improve the model's generalization and explainability.  
While this study represents a modest contribution to the field, it underscores the potential of deep learning models in modernizing fruit quality assessment. By focusing on accessibility, efficiency, and interpretability, this research provides a foundation for developing practical tools that can benefit small-scale farmers and contribute to sustainable agricultural practices.  
Conceptualization, V.Z. and D.C.H.; methodology, V.Z.; software, V.Z.; validation, V.Z.; formal analysis, V.Z.; investigation, V.Z. and D.C.H.; resources, V.Z.; data curation, V.Z.; writing--original draft preparation, V.Z.; writing--review and editing, V.Z. and D.C.H.; visualization, V.Z.; supervision, D.C.H.; project administration, D.C.H.; funding acquisition, D.C.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Sistema Nacional de Investigaciones (SNI) of Panama of the Secretaria Nacional de Ciencia, Tecnologia e Innovacion de Panama (SENACYT). [SNI 24-2024]. The authors would like to thank the Universidad Tecnologica de Panama for their administrative support in the advancement of this project.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.