  Cereal grain plays a crucial role in the human diet as a major source of
essential nutrients. Grain Appearance Inspection (GAI) serves as an essential
process to determine grain quality and facilitate grain circulation and
processing. However, GAI is routinely performed manually by inspectors with
cumbersome procedures, which poses a significant bottleneck in smart
agriculture.
  In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp.
By analyzing the distinctive characteristics of grain kernels, we formulate GAI
as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible
kernels are considered normal samples while damaged grains or unknown objects
are regarded as anomalies. We further propose an AD model, called AD-GAI, which
is trained using only normal samples yet can identify anomalies during
inference. Moreover, we customize a prototype device for data acquisition and
create a large-scale dataset including 220K high-quality images of wheat and
maize kernels. Through extensive experiments, AD-GAI achieves considerable
performance in comparison with advanced AD methods, and AI4GrainInsp has highly
consistent performance compared to human experts and excels at inspection
efficiency over 20x speedup. The dataset, code and models will be released at
https://github.com/hellodfan/AI4GrainInsp.
