GrainBrain: Multiview Identification and Stratification of Defective Grain Kernels

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 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A100</i>) and a deep learning model (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GrainAD</i>). 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.