Crack Detection of Brown Rice Kernel Based on Optimized ResNet-18 Network
Zihao Wang, Zhigang Hu, Xuan Xiao
Abstract
Brown rice kernel cracks significantly impact grain quality, and timely, accurate detection is crucial for enhancing the quality and taste 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.