GrainBrain: Multiview Identification and Stratification of Defective Grain Kernels

Lei Fan, Dongdong Fan, Yiwen Ding, Yong Wu, Donglin Di, Maurice Pagnucco, Yang Song

Abstract: Grain appearance inspection is crucial for evaluating grain quality and determining seed stratification. Typically, trained inspectors manually examine each grain kernel to identify and remove defective ones, which is time-consuming and error-prone. In this article, we present GrainBrain, a robotic vision-based system comprising a hardware prototype (A100) and a deep learning model (GrainAD). A100 is equipped with five cameras to capture high-quality, multiview images of each kernel. The identification of defective kernels is treated as an unsupervised anomaly detection task. GrainAD trains a classifier to distinguish between healthy and pseudoanomaly samples generated at both image and feature levels, and a supervised contrastive learning loss is employed to obtain compact feature representations of healthy kernels. In addition, we release a large-scale dataset containing over 100K annotated images of four types of cereal grains. Extensive experiments were conducted to verify the superiority of our system, achieving an average AUROC of 94.4/90.4% at the image/pixel level. Our system excelled in both efficiency and consistency, as demonstrated by experiments comparing human experts to the system.