Cheng Zhenzhen, Cheng Yifan, Miao Bailing, Fang Tingting, Gong Shoufu
Department of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, China.
Department of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China.
Front Plant Sci. 2025 Mar 12;16:1564301. doi: 10.3389/fpls.2025.1564301. eCollection 2025.
Growing consumer demand for high-quality strawberries has highlighted the need for accurate, efficient, and non-destructive methods to assess key postharvest quality traits, such as weight, size uniformity, and quantity. This study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics. The algorithm develops a fusion expert network architecture that maximizes the use of multimodal features while preserving the distinct details of each modality. Additionally, a novel Heritable Loss function is implemented to reduce redundancy and enhance model performance. Experimental results show that the coefficient of determination (R²) values for weight, size uniformity and number are 0.94, 0.90 and 0.95 respectively. Ablation studies demonstrate the advantage of the architecture in multimodal, multi-task prediction accuracy. Compared to single-modality models, non-fusion branch networks, and attention-enhanced fusion models, our approach achieves enhanced performance across multi-task learning scenarios, providing more precise data for trait assessment and precision strawberry applications.
消费者对高品质草莓的需求不断增长，这凸显了需要准确、高效且无损的方法来评估关键的采后品质特征，如重量、大小均匀度和数量。本研究提出了一种多目标学习算法，该算法利用RGB-D多模态信息来估计这些品质指标。该算法开发了一种融合专家网络架构，在保留每个模态独特细节的同时，最大限度地利用多模态特征。此外，还实现了一种新颖的遗传损失函数，以减少冗余并提高模型性能。实验结果表明，重量、大小均匀度和数量的决定系数（R²）值分别为0.94、0.90和0.95。消融研究证明了该架构在多模态、多任务预测准确性方面的优势。与单模态模型、非融合分支网络和注意力增强融合模型相比，我们的方法在多任务学习场景中实现了更高的性能，为特征评估和精准草莓应用提供了更精确的数据。
