Sensors, Vol. 24, Pages 2467: Development of Multimodal Fusion Technology for Tomato Maturity Assessment

Sensors, Vol. 24, Pages 2467: Development of Multimodal Fusion Technology for Tomato Maturity Assessment Sensors doi: 10.3390/s24082467 Authors: Yang Liu Chaojie Wei Seung-Chul Yoon Xinzhi Ni Wei Wang Yizhe Liu Daren Wang Xiaorong Wang Xiaohuan Guo 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, da...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research