中国农业大学学报2025，Vol.30Issue(4)：51-66,16.DOI:10.11841/j.issn.1007-4333.2025.04.05
基于密集卷积网络的甘薯黑斑病害程度分类
Classification of sweet potato black spot disease severity using dense convolutional networks
摘要
Abstract
To address the problem of sweet potato blackspot disease caused by Ceratocystis fimbriata,which severely impacts tuber quality and thesafety of processed products,this study utilized images of sweet potato black spot disease obtained from controlled cultivation environments.Total phenolic content was selected as an internal quality indicator,while black spot diameter was used as an external appearance indicator.Machine learning techniques were applied to explore the correlation between these two factors,and based on the results,disease severity grading was performed.After comparing the detection performance of ResNet152,VGG19,GoogleNet,EfficientNetB7,MobileNetV2,and DenseNet201 models,a novel algorithm that integrates the Spatial Attention(SA)mechanism,Efficient Channel Attention(ECA)mechanism,and the DenseNet201 network was proposed for the identification of sweet potato black spot disease.The results showed that:1)The Pearson correlation coefficient between black spot diameter and total phenolic content was 0.93,indicating a strong positive correlation(P＜0.01),which suggested that external disease symptoms can effectively reflect internal quality changes;2)Among the models used to identify different disease grades of sweet potato black spot disease,DenseNet201 achieved the highest accuracy of 83.93％;3)The results of Ablation experiments demonstrated that the DenseNet201-SA-ECA model,which incorporated both SA and ECA mechanisms,achieved a classification accuracy of 96.8％on the test set,representing a 12.87％improvement over the original DenseNet201 model and significantly enhancing the precision of black spot disease identification.Combining biochemical indicators with deep learning,this study shows that the DenseNet201-SA-ECA model outperforms other convolutional neural networks in identifying sweet potato black spot disease and can reliably recognize the disease with high accuracy.关键词
甘薯/病害分类/深度学习/总酚质量分数/卷积网络模型Key words
sweet potato/disease classification/deep learning/total phenolic content/convolutional network model分类
农业科技引用本文复制引用
张帅杰,陈思思,戴丹,李艳宏,梁子乐,罗煦钦,霍富龙,胡彦蓉..基于密集卷积网络的甘薯黑斑病害程度分类[J].中国农业大学学报,2025,30(4):51-66,16.基金项目
国家青年科学基金项目(32301585,42001354) （32301585,42001354）
浙江省重大科技专项重点农业项目(2015C02047) （2015C02047）
