## 1 Introduction  
Grain storage systems are crucial for ensuring food security. However, global grain storage losses amount to approximately 8% annually, with even more substantial losses among dispersed rural households due to inadequate storage conditions. Storage losses are primarily influenced by two factors: moisture and temperature. Moisture impact is particularly significant, as excessive moisture can lead to localized hotspots (deteriorating regions with elevated temperatures and moisture) within storage facilities. These conditions trigger multiple issues, including seed germination, mold growth, and pest infestation [1]. Currently, effective methods for the timely detection of these anomalies in deep grain storage areas remain lacking. In situ moisture detection techniques for grain piles primarily include conventional sensors (temperature, moisture, or chemical sensors) [2, 3], near-infrared moisture measurement [4], acoustic temperature detection, and microwave scattering methods [5]. The first three approaches share a common limitation: shallow detection depth. This constraint arises from two main factors: (1) grain's excellent thermal insulation properties, which limit sensor sensitivity to nearby grains only; and (2) the limited penetration capabilities of near-infrared and acoustic methods, restricting their application to surface-level detection. These limitations make it difficult to detect hotspots in deep grain layers. In contrast, microwave-based methods show promising potential for deep-layer hotspot detection, primarily due to microwaves' superior penetration capability. Current microwave-based methods for detecting moisture anomalies in grain piles primarily comprise two approaches: electromagnetic imaging reconstruction [6; 7; 8; 9; 10; 11] and electromagnetic wave reflection analysis [12; 13; 14]. For instance, [PERSON] et al. [7; 8] at the University of Manitoba applied electromagnetic imaging to grain inspection, developing a finite element contrast source inversion (FEM-CSI) method and designing thin coaxial semi-circular antennas for hotspot detection in industrial-scale grain silos. However, significant challenges persist. Methods using [PERSON]'s equations to describe microwave scattering phenomena face inherent complexities [15], including solution non-uniqueness due to non-radiating currents, non-linearity associated with multiple scattering effects, ill-conditioned problems, and high computational costs [16]. These challenges can result in processing times of several hours and potential artifacts. [PERSON] et al. [17] also applied their method to granaries. Similar to humidity sensors, the accuracy of radio-frequency moisture sensors for grain moisture detection was affected to varying degrees by ventilated granary conditions. Despite microwaves' strong penetration capability, electromagnetic reflection methods for moisture assessment typically achieve only shallow detection depths [12; 14]. For example, [PERSON] et al. [12] developed an open-ended coaxial wave-guide sensor for direct insertion into grain piles for moisture measurement. However, this sampling-based approach has significant limitations: it may miss anomalies when multiple irregular regions exist, and it proves ineffective for detecting deep-layer anomalies within grain piles. In summary, deep-layer moisture anomaly detection in grain piles faces multiple challenges, including artifacts, slow computation speeds, limited detection depth, and susceptibility to external interference. Beyond the aforementioned methods, digital beamforming offers an alternative approach to electromagnetic imaging. As a readily implementable and rapid imaging technique, digital beamforming has been successfully applied in radar systems, sonar systems, seismological research, and medical imaging [18; 19; 20]. This paper presented a novel application of digital beamforming technology [21] to grain moisture detection, employing the delay-multiply-and-sum (DMAS) algorithm [20; 22] for rapid localization of moisture anomalies in grain piles.