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The occurrence of cracks in brown rice kernels has a substantial impact on grain quality. The timely and accurate detection of rice grains with cracks is crucial for enhancing the overall quality and flavor of processed rice. In this study, we developed an optical observation platform and optimized the original ResNet-18 neural network structure to improve the detection and classification of grain cracks. We established image datasets for japonica and indica rice varieties, and employed image augmentation and model migration techniques during training. In addition, we compared the performance of the optimized model with DenseNet-121 and GoogLeNet. The results demonstrate a notable enhancement in crack detection accuracy for japonica, reaching 96%, which is a 3.67% improvement over the original model. Furthermore, we achieved a substantial reduction in average training time, reduced by 58.66%. For indica rice, after model optimization and migration, the accuracy reached 96.67%. It’s important to note that the optimized model has limitations and is not suitable for mixed datasets with limited training data. This technology offers the capability to accurately identify and detect cracks in brown rice kernels under visible light conditions, presenting a promising solution for enhancing grain quality during processing.

The microscopic and macroscopic structure of brown rice kernel determines that it is a complex bio-material. Therefore, revealing kernel structural characteristics and fracture mechanisms can provide a reference for related processing application situations. In this study, an in-situ observation method with the multi-layer structure optical platform was developed to better understand the kernel fracture behavior. SEM images show that the starch structure distribution inside the endosperm layer could be divided into core, transition and outer layers. Meanwhile, kernel interior crack formation mechanism was revealed under quasi-static compression and verified from the perspective of fracture and structural mechanics. Typical kernel contact surface expansion behavior under compression conditions was analyzed. The results indicate that the compressive strength of glutinous kernel (56.3 MPa) larger than indica (48.1 MPa), and it was affected by kernel surface dimension and microscopic structure. The study aims to in-depth analysis fracture mechanism of brown rice kernel during the processing, providing a methodological reference for bio-material fracture. [Display omitted] • An in-situ observation method and test setup established for brown rice kernel (BRK) fracture behavior. • SEM revealed the structural distribution of starch granules inside the endosperm layer. • Fracture mechanism of BRK was influenced by combined macroscopic dimension and microstructure. • Compressive strength of BRK was affected by amylopectin content inside the kernel. Compressive strength of BRK was affected by amylopectin content inside the kernel. [ABSTRACT FROM AUTHOR]