IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Liu, Bohan; Ge, Ruixing; Zhu, Yuxuan; Zhang, Bolin; Zhang, Xiaokai; Bao, Yanfei

doi:10.3390/s23198134

Cited by 1 publication

(1 citation statement)

References 45 publications

(69 reference statements)

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“…A TP (True Positive) result indicated that the actual situation was a positive situation and the prediction was positive, i.e., the prediction was correct, and the same result could be obtained for TN (True Negative), FP (False Positive), and FN (False Negative) results [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Development of Multimodal Fusion Technology for Tomato Maturity Assessment

Liu,

Wei,

Yoon

et al. 2024

Sensors

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The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes. The method uses feature fusion to integrate feature information from images, near-infrared spectra, and haptic modalities into a unified feature set and then classifies the maturity of tomatoes through deep learning. Each modality independently extracts features, capturing the tomatoes’ exterior color from color images, internal and surface spectral features linked to chemical compositions in the visible and near-infrared spectra (350 nm to 1100 nm), and physical firmness using haptic sensing. By combining preprocessed and extracted features from multiple modalities, data fusion creates a comprehensive representation of information from all three modalities using an eigenvector in an eigenspace suitable for tomato maturity assessment. Then, a fully connected neural network is constructed to process these fused data. This neural network model achieves 99.4% accuracy in tomato maturity classification, surpassing single-modal methods (color imaging: 94.2%; spectroscopy: 87.8%; haptics: 87.2%). For internal and external maturity unevenness, the classification accuracy reaches 94.4%, demonstrating effective results. A comparative analysis of performance between multimodal fusion and single-modal methods validates the stability and applicability of the multimodal fusion technique. These findings demonstrate the key benefits of multimodal fusion in terms of improving the accuracy of tomato ripening classification and provide a strong theoretical and practical basis for applying multimodal fusion technology to classify the quality and maturity of other fruits and vegetables. Utilizing deep learning (a fully connected neural network) for processing multimodal data provides a new and efficient non-destructive approach for the massive classification of agricultural and food products.

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Section: Methodsmentioning

confidence: 99%