# Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations
## 4 Discussion  
Recent studies have demonstrated the effectiveness of deep learning models in fruit quality detection and grading, highlighting their potential to improve accuracy and efficiency in agricultural practices [33, 34]. Previous studies have demonstrated the efficacy of deep learning models in fruit quality assessment, often emphasizing accuracy in controlled environments. Direct comparisons with numerical results are difficult due to differences in datasets or experimental setups. Our approach, while modest in its scope, demonstrates comparable accuracy using a more constrained dataset, reflecting real-world scenarios faced by small-scale producers. Figure 8: Model history diagram using accuracy and loss with 5 epochs. Figure 7: Model history diagrams using accuracy and loss with 50 epochs. This suggests that even with limited data, our method can achieve practical results, which is crucial for resource-limited settings. The transfer learning model based on MobileNetV2, as well as the custom model, reached high accuracy within just one epoch. This rapid convergence presents possibilities but also raises some concerns. On the one hand, it indicates that the models can learn patterns efficiently. However, it also points to the potential limitations of the dataset in terms of size and diversity. The rapidly achieved near-perfect accuracy of both models is indicative of the dataset potentially having highly similar images, which may not comprehensively represent the diverse conditions under which the model would be expected to operate in real-world scenarios. In essence, the rapid convergence to high accuracy may reflect potential overfitting to a dataset that might not be sufficiently challenging or diverse. Notably, despite these concerns, the custom model, with its simpler architecture, had comparable performance to the transfer learning model with this dataset. This highlights the utility of exploring even basic models for the initial stages of development in fruit quality assessment systems, especially when resources are limited, as is the case for small-scale farmers and practitioners. The proposed approach is scalable, as it can be trained on larger, more diverse datasets with more resources, potentially further improving accuracy and generalization. Deployment on edge devices for real-time assessment and incorporating temporal information for tracking ripening are directions for future research. Additionally, in the future, similar methods can be used as base models for transfer learning in applications specific to the fruit domain. The feature maps used for the identification of damaged areas in fruits revealed that the model's neurons tend to activate around damaged spots on the fruit, indicating that the model focused on relevant features such as texture changes or discoloration. This enhances the understanding of the model's decisions for end users, especially small-scale farmers. For instance, if the model classifies fruit as damaged, the visualization can show the specific areas that influenced this decision, making the tool more transparent and thus enhancing its reliability. This approach helps ensure that the AI model is not just a \"black box\" but a tool that users can feel confident using, knowing that its decisions are based on visible and understandable features. By making these tools more interpretable, the goal is to support wider adoption among users with varying levels of technical expertise. Fruit quality assessment is vital for farmers, consumers, and retailers. An automated, accurate assessment tool can contribute to ensuring high standards of produce, reducing waste, and optimizing supply chains. Furthermore, this holds global implications for food safety and sustainable agriculture, particularly benefiting small-scale farmers in resource-constrained settings. The models' performance highly depends on the quality and diversity of the dataset.