Vegetable and fruit freshness detection based on deep features and principal component analysis

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Rose tea is a type of flower tea in China's reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.

Fruit freshness detection based on multi-task convolutional neural network

Background

Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a deep convolutional neural work for fruit freshness detection.

Results

We design an MTL model that optimizes the freshness detection (T1) and fruit type classification (T2) tasks in parallel. The model uses a shared CNN (convolutional neural network) subnet and two FC (fully connected) task heads. The shared CNN acts as a feature extraction module and feeds the two task heads with common semantic features. Based on an open fruit image dataset, we conducted a comparative study of MTL and single-task learning (STL) paradigms. The STL models use the same CNN subnet with only one specific task head. In the MTL scenario, the T1 and T2 mean accuracies on the test set are 93.24% and 88.66%, respectively. Meanwhile, for STL, the two accuracies are 92.50% and 87.22%. Statistical tests report significant differences between MTL and STL on T1 and T2 test accuracies. We further investigated the extracted feature vectors (semantic embeddings) from the two STL models. The vectors have an averaged 0.7 cosine similarity on the entire dataset, with most values lying in the 0.6-0.8 range. This indicates a between-task correlation and justifies the effectiveness of the proposed MTL approach.

Conclusion

This study proves that MTL exploits the mutual correlation between two or more relevant tasks and can maximally share their underlying feature extraction process. we envision this approach to be extended to other domains that involve multiple interconnected tasks.