The detection of fish freshness is crucial for ensuring food safety. This study addresses the limitations of traditional detection methods, which rely on laboratory equipment and complex procedures, by proposing a smartphone-based detection method, termed FreshFusionNet, that utilizes a pitaya peel pH intelligent indicator film in conjunction with multimodal deep learning. The pitaya peel indicator film, prepared using high-pressure homogenization technology, demonstrates a significant color change from dark red to yellow in response to the volatile alkaline substances released during fish spoilage. To construct a multimodal dataset, 3600 images of the indicator film were captured using a smartphone under various conditions (natural light and indoor light) and from multiple angles (0° to 120°), while simultaneously recording pH values, total volatile basic nitrogen (TVB-N), and total viable count (TVC) data. Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. Experimental results indicate that the overall classification accuracy of FreshFusionNet reaches 99.61%, with a single inference time of only 142 ± 40 milliseconds (tested on Xiaomi 14). This method eliminates the need for professional equipment and enables real-time, non-destructive detection of fish spoilage through smartphones, providing consumers and the food supply chain with a low-cost, portable quality-monitoring tool, thereby promoting the intelligent and universal development of food safety detection technology.

In the context of the implementation of the rural revitalization strategy in China, returning rural migrant workers are bound to have a certain impact on the rural economy, and land is a very important factor in the agricultural economy. Using data from the 2018 China Labor Dynamic Survey (CLDS), this study examines how migrant workers’ return behaviors influence farmland transfer-in. To address potential endogeneity, the analysis employs the Probit model, instrumental variable methods, and propensity score matching. The findings reveal that returning migrant workers significantly promote farmland transfer-in. Households with returning migrant workers exhibit stronger demands for land transfer-in and tend to operate farmland on a larger scale. Furthermore, returning migrant workers drive farmland expansion through mechanization labor substitution, enhanced access to agricultural loans, and reduced non-farm participation. Additionally, returning migrant workers who are highly educated and younger play a particularly influential role, underscoring the heterogeneous impacts across different migrant groups. This study provides empirical evidence for rural revitalization policies in China by systematically analyzing the effect of returning migrant workers in promoting land transfer-in and the path of influence on farmland scale.