数字农科院2.0

A novel cross-modal decoupling dual-spectral fusion system and method: A case study on the on-site estimation of fresh tobacco leaf curing characteristics

文献类型： 外文期刊

作者： Zhongtao Huang;Shichang Wang;Rongguang Zhu;Yapeng Kang;Lingfeng Meng;Jie Ren

作者机构：

关键词： Cross-modal fusion;Curing characteristics;Dual spectral system;Fresh tobacco leaf;Modality feature decoupling;Multi-task learning

期刊名称： Industrial Crops and Products

ISSN： 0926-6690

年卷期： 2025 年 237 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Accurate and timely estimation of easy curing characteristic (EaCC) and endurable curing characteristic (EnCC) of fresh tobacco leaves is crucial for subsequent curing and processing. This study developed a dual-spectral system for on-site estimation of EaCC and EnCC grades. The system integrated a fiber-optic spectrometer, halogen lamp, and 450 nm laser to obtain chlorophyll fluorescence (ChlF) and visible-near infrared (VNIR) spectra via mode switching. Given the unequal contributions of ChlF and VNIR spectra, a cross-modal collaborative decoupling network driven by multitasking (CMCDN-MT) model was designed to fuse ChlF and VNIR spectra for simultaneous estimation of EaCC and EnCC grades. CMCDN-MT integrated two independent subnetworks and a branch-interactive subnetwork to extract single-modal and cross-modal spectral features under the supervision of a trilateral clustering loss function. A task-driven modality fusion (TDMF) module was further designed to adaptively reorganize sub-task preference features, reducing conflicts in multitasking. Compared with single-spectral, single-task, traditional machine learning, and advanced deep learning models, the CMCDN-MT model achieved the best test performance. The dual-spectral system deployed with the CMCDN-MT model achieved 100.00 % precision in identifying low-quality EaCC and EnCC tobacco leaves, with overall accuracies of 88.00 % for EaCC grades and 86.00 % for EnCC grades, respectively. Due to the use of a multi-task strategy, the CMCDN-MT model simultaneously estimated both EaCC and EnCC grades for a sample in just 3.35 ms. Overall, the proposed method shows the potential for online applications and offers an effective approach for multi-attribute quality assessment of other industrial crops.

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