Abstract: Rice is one of the most widely cultivated staple food crops worldwide, feeding over half of the global population and serving as a core commodity in international grain trade. Accurate and rapid determination of its moisture content and bulk density is a fundamental requirement across the entire post-harvest supply chain, from field harvesting and on-site purchase to warehouse storage and industrial processing. Improper moisture content, especially excessive moisture during storage, directly triggers fungal proliferation, mycotoxin contamination, and grain deterioration, resulting in severe post-harvest losses and food safety risks. Meanwhile, bulk density, as a key indicator of grain physical quality, directly determines warehouse storage capacity calculation, trade pricing, and processing yield evaluation. Deviations in bulk density measurement can cause economic losses in grain circulation and misjudgment of processing suitability. Traditional detection methods for these two parameters still have prominent technical bottlenecks. The standard oven drying method, recognized as the reference method for moisture measurement, requires long drying time (usually 4–24 h) and destructive sample preparation, making it impossible to meet the demand for on-site rapid detection. The conventional volumetric method for bulk density testing relies on manual operation with poor repeatability and low efficiency. Emerging techniques such as near-infrared spectroscopy have high detection speed but are susceptible to grain surface state, sample particle size and ambient light, with poor stability in complex field environments. Microwave transmission methods, although capable of non-destructive detection, require microwave penetration through the entire sample, limiting their application in thick grain piles. The existing fixed-frequency microwave traveling-standing wave method relies on motor-driven mechanical scanning to obtain spatial wave distribution, which suffers from reduced long-term stability due to mechanical vibration and component wear, hindering its popularization in grassroots grain depots and acquisition sites. To address the above technical shortcomings, this study proposes a novel narrow-band frequency sweep microwave reflection method for the simultaneous rapid non-destructive detection of rice moisture content and bulk density, and develops a corresponding portable detection device. A portable C-band microwave reflection measurement device operating in the frequency range of 5 640–5 920 MHz was developed, mainly composed of a voltage-controlled microwave oscillator, power divider, microwave mixer, circulator, planar microstrip antenna, microcontroller unit and digital-to-analog converter. Different from the traditional spatial mechanical scanning scheme, this study innovatively introduces narrow-band frequency sweep technology: by continuously adjusting the microwave operating frequency in a limited bandwidth, the traveling-standing wave characteristic parameters at a fixed detection position are extracted, which completely replaces the mechanical displacement scanning structure, cancels moving parts such as motors and guide rails, and significantly improves the long-term operational stability of the device. Meanwhile, the device adopts a single-side reflection detection mode without the need for microwave penetration through the sample, and does not require expensive vector network analyzers, greatly reducing the hardware cost. In this study, japonica rice samples with seven moisture content gradients (8.2%–20.1%) were prepared, and each moisture content group was set with two bulk density states (loose and compact) to cover the common bulk density range of 0.546–0.645 g/cm³ in actual grain circulation. The response laws of standing wave ratio and antinode frequency to moisture content and bulk density were systematically analyzed, and binary linear regression models for simultaneous prediction of the two parameters were constructed based on the two characteristic parameters. Independent on-site validation tests were carried out at the Jilin Provincial Grain and Oil Testing Center using 12 groups of representative rice samples covering the full range of moisture content and bulk density in actual grain purchase and storage scenarios. Each sample was measured repeatedly 3 times to evaluate the repeatability of the device. The validation results showed that the moisture content prediction model had a coefficient of determination (R²) of 0.989 and a standard error of prediction (SEP) of 0.455 1%, and the bulk density prediction model had an R² of 0.986 and an SEP of 0.005 7 g/cm³. The single-sample detection process, including signal acquisition, data processing and result output, could be completed within 5 seconds without any sample pretreatment such as grinding or weighing. Compared with existing microwave transmission methods and fixed-frequency mechanical scanning methods, the proposed method has higher detection accuracy, better long-term stability and lower equipment cost. Overall, the proposed method and device integrate the advantages of non-destructive detection, high accuracy, fast response, good portability and low cost, effectively solving the technical problems of low stability and high cost of traditional microwave detection devices. It provides a reliable technical solution for on-site rapid detection of rice quality parameters in grain purchase, warehousing and processing links, and has broad application prospects in the construction of intelligent grain storage and digital grain circulation systems.