Determining the geographical origins of goji berries using the Twin-Tower model for Multi-Feature

Abstract

Gogi berry products, known for their medicinal value, are exclusively available in the Ningxia region of China. However, the nutritional components, active ingredients, and economic value of these berries vary considerably among different cultivation zones in Ningxia. Accurate geographical traceability is essential not only for fostering the sustainable growth of the goji berry industry but also to ensure consumer safety, safeguard regional brands, and prevent fraudulent labelling practices that can erode market confidence and disrupt economic stability. Consequently, identification of the precise geographical origin of goji berries in Ningxia has emerged as a focal point in the progression of the industry. In this study, the Twin-Tower Model (TTM) was employed in conjunction with hyperspectral spectral and image data to establish a classification model for determining the proximate geographical origins of goji berries in Ningxia, including the regions of Zhongning, Guyuan, Tongxin, and Huinong. The results show that the graph-based training method built on TTM improves the accuracy by 3.7% compared to traditional data fusion methods. Notably, it attained a flawless 100% recognition rate for the highest quality Zhongning goji berries. These findings highlight that the implementation of the TTM method for multitask learning signifies a novel direction for transforming the conventional data fusion paradigm. Furthermore, it provides an essential technological means for the robust development of the goji berry industry.