沈阳农业大学学报2025，Vol.56Issue(1)：82-91,10.DOI:10.3969/j.issn.1000-1700.2025.01.009
基于深度学习的稻粒在穗计数方法研究
Study on Counting Method of on Panicle Rice Grains Based on Deep Learning
摘要
Abstract
[Objective]Rice grain counting is a crucial step in rice seed testing.To address the inefficiencies and errors associated with traditional manual counting of rice grains,this study constructed an in-situ counting model for rice grains.In-situ counting method can not destroy the original topological structure of rice panicles,and then it can be further applied to obtain other phenotypic parameters.[Methods]The model employs ResNet as its backbone network and predicts the probability density distribution of rice grains by leveraging the feature correlation between image and rice grain exemplars.Subsequently,the number of rice grains is obtained by summing the density maps.An image dataset of rice panicles was collected,and a loss function tailored for rice grain counting on panicles was defined.This function takes into account both the consistency between the predicted density map and the actual rice grain distribution,as well as the relevant constraints of the exemplar labeling box.The performance of the model was evaluated using Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and Mean Relative Error(MRE).Experimental the proposed model is reduced by 2.2%.When compared to the SAM(Segment Anything Model)based on instance segmentation,the MRE of the proposed model decreases by 12.2%,and compared to the T-Rex2 model,it is reduced by 6.5%.[Conclusion]This research method is based on the deep learning model,which can automatically identify and count rice grains in the image and improve the counting efficiency.At the same time,compared with other deep learning models,this research model has stronger learning ability with few samples.The method presented in this paper can be effectively applied to the task of rice grain counting in panicles,and the research provides valuable insights for obtaining phenotypic parameters of rice panicles.关键词
稻穗表型/原位计数/水稻/特征提取/表型参数Key words
phenotype of rice panicle/in-situ counting/rice/feature extraction/phenotypic parameter分类
计算机与自动化引用本文复制引用
周云成,张羽,刘泽钰,李瑞阳..基于深度学习的稻粒在穗计数方法研究[J].沈阳农业大学学报,2025,56(1):82-91,10.基金项目
国家重点研发计划项目(2021YFD1500204,2023YFD1501303) （2021YFD1500204,2023YFD1501303）
