Rapid and non-destructive techniques for fruit quality evaluation are widely concerned in modern agro-industry. Spectroscopy is one of the most commonly used techniques in this field. With the growing popularity of various spectroscopic instruments, it is indeed worthwhile to explore modeling with multi-source spectral data to achieve more accurate predictions. Nonetheless, a major challenge is acquiring enough labeled samples, as measuring fruit chemical values is laborious, expensive, and time-consuming, which hinders the development of a reliable prediction model. Therefore, this study aims to develop a model for predicting the internal chemical composition of fruits by integrating multi-source spectral fusion combined with self-supervised learning (SSL). A visible (Vis) and near-infrared (NIR) spectral dataset related to dry matter content (DMC) prediction in mango fruit is used as an example to validate the effectiveness of the proposed method. To obtain multi-source spectral data, the Vis and NIR portions are processed as two separate spectral ranges. An SSL pre-training is performed utilizing a large amount of raw unlabeled spectral data to extract general knowledge, which is subsequently migrated to a downstream task for fine-tuning. The experimental results indicate that the multi-source spectral fusion model performs better than the single-source spectral model. Moreover, SSL solves the data scarcity problem and outperforms non-pre-trained models in downstream DMC prediction tasks with less computational overhead. Remarkably, utilizing only <10 % of the total samples is sufficient to achieve a performance close to 99 % of the best results. The presented method has great potential in spectral analysis of food and agro-products.

中文翻译：

---

基于自监督学习的多源光谱融合果实品质评价——以芒果果实成熟度预测为例


用于水果质量评估的快速和非破坏性技术在现代农业工业中受到广泛关注。光谱学是该领域最常用的技术之一。随着各种光谱仪器的日益普及，探索使用多源光谱数据进行建模以实现更准确的预测确实是值得的。尽管如此，一个主要挑战是获得足够的标记样品，因为测量水果的化学值既费力、昂贵又耗时，这阻碍了可靠预测模型的开发。因此，本研究旨在通过整合多源光谱融合结合自监督学习 （SSL） 来开发一种预测水果内部化学成分的模型。以芒果果实干物质含量 （DMC） 预测相关的可见光 （Vis） 和近红外 （NIR） 光谱数据集为例，验证了所提方法的有效性。为了获得多源光谱数据，可见光和近红外部分被处理为两个独立的光谱范围。SSL 预训练利用大量原始未标记的光谱数据来提取一般知识，然后将其迁移到下游任务进行微调。实验结果表明，多源光谱融合模型的性能优于单源光谱模型。此外，SSL 解决了数据稀缺问题，并在下游 DMC 预测任务中以更少的计算开销优于非预训练模型。值得注意的是，仅使用总样品的 <10 % 就足以实现接近 99 % 的最佳结果的性能。所提出的方法在食品和农产品的光谱分析中具有很大的潜力。