Sustainable deep vision systems for date fruit quality assessment using attention-enhanced deep learning models

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Introduction: Accurate and automated fruit classification plays a vital role in modern agriculture but remains challenging due to the wide variability in fruit appearances. Methods: In this study, we propose a novel approach to image classification by integrating a DenseNet121 model pre-trained on ImageNet with a Squeeze-and-Excitation (SE) Attention block to enhance feature representation. The model leverages data augmentation to improve generalization and avoid overfitting. The enhancement includes attention mechanisms and Nadam optimization, specifically tailored for the classification of date fruit images. Unlike traditional DenseNet variants, proposed model incorporates SE attention layers to focus on critical image features, significantly improving performance. Multiple deep learning models, including DenseNet121+SE and YOLOv8n, were evaluated for date fruit classification under varying conditions. Results: The proposed approach demonstrated outstanding performance, achieving 98.25% accuracy, 98.02% precision, 97.02% recall, and a 97.49% F1-score with DenseNet121+SE. In comparison, YOLOv8n achieved 96.04% accuracy, 99.76% precision, 99.7% recall, and a 99.73% F1- score. Discussion: These results underscore the effectiveness of the proposed method compared to widely used architecture, providing a robust and practical solution for automating fruit classification and quality control in the food industry.

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