There are often agricultural product quality problems in the production and circulation of agricultural products. Therefore, there are more and more people on the agricultural product supply chain based on the Internet of things. This article mainly introduces the research on the perception data fusion of agricultural product supply chain in the context of the Internet of things. This is a simple research result based on the Internet of things technology platform, which analyzes the current status of the product according to market demand. After analysis and comparison, a sensory data fusion model suitable for the supply chain of agricultural products is obtained, and information technology based on the Internet of things is used to transform and optimize the Internet of things in the circulation of agricultural products. The experimental results of this article show that data fusion technology based on the Internet of things can solve and track 69.45% of the problem of unknown sources of agricultural products, improve the supply efficiency of agricultural products by 43%, reduce the health problems of agricultural products by 31.24%, and reduce the prices of agricultural products by 13–20%. Improving logistics efficiency can save 5 million tons of agricultural products.

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Soil enzymes strongly affect soil organic carbon (SOC) and nitrogen (TN) storage. However, few studies have focused on their relationships in aggregates, especially in sodic-alkali agricultural fields. In the current study, we hypothesized that the impact of soil enzymes on SOC and TN were different within aggregates for their heterogeneous distribution. Soils collected from the surface (0–20 cm) and subsurface (20–40 cm) layers of sodic-alkali agricultural fields in the northeast of China were separated via the dry sieve method into macro-aggregates (>2000 μm), meso-aggregates (250–2000 μm), and micro-aggregates (<250 μm). SOC, TN, microbial biomass carbon (MBC) and nitrogen (MBN), and C- and N-cycling enzymes, namely amylase (AMY), invertase (INV), β-glucosidase (GLU), catalase (CAT), β-N-acetylglucosaminidase (NAG), and urease (URE) in soil aggregates were tested and analyzed. High content of SOC and TN were observed in macro- and meso-aggregates in both layers, with the largest amount detected in meso-aggregates. The highest values of MBC and MBN were observed in meso-aggregates, followed by micro-aggregates for MBC and macro-aggregates for MBN. Soil enzymes were distributed heterogeneously in soil aggregates, where the activities of AMY, INV, and URE in both layers were in the order of meso-aggregates > macro-aggregates > micro-aggregates. The same trend was followed by NAG of surface soils, while in the subsurface soils, NAG activities increased with the increasing aggregate sizes. NAG activities in both layers decreased with decreasing aggregate sizes. The GLU activity rose with the decreasing aggregate sizes in both layers, contrary to CAT. Enzyme activities affect SOC and TN in soil aggregates, for NAG, INV, GLU, and URE are closely related to SOC and TN across aggregate sizes. The test indices mentioned above in the surface layer were higher than those in the subsurface layer. These results indicate that biophysical processes associated with C- and N-cycling enzymes may be vital to the SOC and TN sequestration within soil aggregates in sodic-alkali agricultural fields.

The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer-review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings, the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.Xu Sun and Kunliang Shu have not responded to correspondence regarding this retraction. Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

With the continuous development of the logistics industry and big data technology, the integration of traditional industries and the Internet of Things has become the trend of the times. However, agricultural product quality problems occur from time to time in multiple links such as the production and circulation of agricultural products. Therefore, the agricultural product supply chain based on the Internet of Things has gradually received attention. The purpose of this paper is to study the application of perception data fusion of agricultural products supply chain based on internet of things. In order to study the application of perception data fusion of agricultural products supply chain based on Internet of things, this paper analyzes the status quo of agricultural products industry traceability platform and extracts other product supply modes based on the Internet of things technology. After analysis and comparison, it comes to the perception data fusion mode suitable for the agricultural product supply chain, and uses the information technology based on the internet of things to carry out the agricultural product circulation process Internet of things transformation and process optimization. The results show that the application of data fusion based on the internet of things can solve 69.45% of the agricultural products whose origins are unknown and cannot be traced, improve the supply efficiency of agricultural products by 43%, reduce the health problems of agricultural products by 31.24%, reduce the prices of agricultural products by 13%-20%, improve the logistics efficiency and save about 5 million tons of agricultural products. Therefore, it is necessary to study the application of agricultural product perception data fusion based on internet of things.