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Design and Implementation of a Digital Technology System for Large-Scale Soil Survey Information
Scientia Agricultura Sinica 2026, 59(17): 3807-3821
Published: 01 September 2026
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Objective

Soil survey involves investigations on soil formation conditions, soil types, soil quality, soil utilization and its potential. Given their extensive scope, multi-departmental involvement, heavy workload, and high technical demands, enhancing work efficiency and ensuring data quality have become critical challenges in conducting soil surveys. Based on an in-depth analysis of the soil survey business, supported by information and digital technologies, and in combination with the third national soil census (hereinafter referred to as "the third soil survey"), this paper designed an information system for the third soil survey, developed an information work platform for the third soil survey, and realized the information recording, digital management and visual dispatching of the third soil survey work.

Method

According to the working characteristics of the soil survey, an information technology system composed of one desktop system and four mobile apps (investigation and sampling, sample transfer, quality control, and expert working) was designed, and an information work platform composed of four layers: infrastructure layer, data layer, platform layer, and user layer. In terms of technical implementation, the on-site limitation of sampling points was realized based on a virtual electronic fence, the intelligent comparison of detection data was realized based on threshold determination, and the dynamic sample data visualization was realized based on Cesium. In terms of platform functions, Java programming language and front-end and back-end separation technology were used to realize various functions. The desktop system realized a map of sample task management, sample preparation management, sample detection management, quality control, technical guidance, full traceability and other functional modules. The survey and sampling APP realized the functions of sample task change, sample point navigation, code scanning binding, data recording, offline data saving, and data submission. The sample flow APP realized the functions of sample flow progress query, sample receiving, sample packing, sample sending, sample receiving, sample testing, etc. The quality control APP realized the functions of investigation and sampling verification, sample preparation and inspection, test and laboratory flight inspection, on-site video recording, and inspection item comparison. The expert working APP realized the functions of task allocation, task execution, task check-in and viewing for various areas.

Result

Since the trial operation in June 2022, the platform process has been relatively clear, the functions have been relatively perfect, the reliability has been high, and the operation has been stable. Until the end of August, 2025, there were 81000 registered users, 5.6 million visits, 400 million pieces of data collected and managed from various samples, a cumulative data volume of about 241TB, and an average response time within 2 s.

Conclusion

Designed and developed with support from geographic information systems, global positioning systems, intelligent workflows, microservices architecture, and other information and digital technologies, the soil survey information platform has effectively enhanced the efficiency of soil survey operations, reduced survey costs, and improved data quality. It has provided comprehensive, all-element information support for conducting the third soil survey.

Open Access Issue
Predictive Modeling for Nondestructive Determination of Soluble Solids Content in Kiwifruits Based on Optimized Regional Features of Hyperspectral Images
Food Science 2025, 46(24): 1-8
Published: 25 December 2025
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This study systematically quantified and analyzed the correlation between different regions of interest (ROI) characteristics (part, shape, and size) and the prediction accuracy of soluble solids content (SSC) in kiwifruits to build a kiwifruit SSC prediction model by integrating hyperspectral imaging with ROI selection. The ROI spectral data of whole fruits were preprocessed using multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, standard normal variate (SNV) transformation, or SNV-SG smoothing. A partial least squares regression model was established to predict the SSC of kiwifruits, and performance analysis was conducted to determine the optimal preprocessing strategy. Furthermore, we extracted the ROI spectral information of different shape and size combinations at the equator, calyx, and peduncle of kiwifruits to compare the accuracy of the prediction model. The results revealed that SNV preprocessing yielded the best performance, with a coefficient of determination (RP2) of 0.8327 and a root mean square error of prediction (RMSEP) of 0.3871 for whole-fruit ROI prediction set. The ROI characteristics significantly impacted the accuracy of SSC prediction, and the effects of fruit part, shape, and size followed the decreasing order: equator > calyx > pedicel; circular > square; and small > large. Notably, the small circular ROI at the equator yielded the optimal prediction, with RP2 = 0.9173 and RMSEP = 0.2217. This study demonstrates the crucial role of ROI optimization in hyperspectral image modeling, clarifies the advantages of the “equator-circular-small” combination, and provides an effective approach for improving the prediction accuracy of SSC in kiwifruits using hyperspectral technology.

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