AV4GAInsp: An Efficient Dual-Camera System for Identifying Defective Kernels of Cereal Grains

Grain Appearance Inspection (GAI) is a pre- requisite for grain quality determination, providing guidance for grain processing, storage and trade. GAI is routinely performed by trained inspectors who are required to visually inspect cereal grains for each individual kernel. Since grain kernels (e.g., wheat, rice) are tiny with heterogeneous shapes and appearance, manually performing GAI is time-consuming and error-prone. This paper presents a machine vision-based customization of an automated system for grain appearance inspection, called AV4GAInsp, which consists of a device and an analysis framework. The device is equipped with an elabo- rate feeding module and a capturing module for automatically pre-processing grain kernels and efficiently acquiring high- quality images for these kernels. The framework employs deep convolutional neural networks to process these captured images to classify the kernels as normal or defective. We also built and released a large-scale dataset, named GrainDet, that includes over 140K images for three types of grains: wheat, sorghum and rice. Comprehensive experiments are conducted to validate the efficacy and performance of our AV4GAInsp system, achieving an average F1-score of 98.4% and excelling at inspection efficiency by over 20× speedup. Kappa statistic tests are performed to confirm the consistency between our system and human experts. It is expected that AV4GAInsp will alleviate inspectors’ workloads and inspire further research in smart agriculture. The project can be found at https://github.com/hellodfan/GrainDet.