摘要
双孢蘑菇疣孢霉病导致菇农巨大的经济损失,针对其早期症状不明显且缺乏有效的病害诊断方法,将显微高光谱成像技术应用于双孢蘑菇疣孢霉病早期检测研究。首先借助神经网络强大的非线性学习能力,采用基于注意力机制和稀疏自编码器重建网络的BS-Net-FC算法对厚垣孢子显微高光谱图像波段选择,并提出基于厚垣孢子目标形态特异性的MTCEM算法检测厚垣孢子目标,结果显示波段选择算法保证波段子集的信息量同时有效降低了冗余波段,在波段数为17的波段子集上MTCEM算法AUC达到最佳为0.8785。本方法能够有效减少数据冗余以及对厚垣孢子目标检测效果良好,为双孢蘑菇疣孢霉病早期检测提供新思路和技术支持。
The mycogone perniciosa of Agaricus bisporus caused huge economic losses to mushroom farmers.In view of the unobvious early symptoms and the lack of effective disease diagnosis methods,the microscopic hyperspectral imaging technology was applied to the early detection of the mycogone perniciosa of Agaricus bisporus.Firstly,the BS-Net-FC algorithm based on the attention mechanism and the sparse auto-encoder reconstruction network was used to select the band of microscopic hyperspectral image of chlamydospore by means of the strong nonlinear learning ability of neural network.The MTCEM algorithm based on the morphological specificity of chlamydospore targets was proposed to detect the chlamydospore targets.The results showed that the band selection algorithm ensured the information of the band subset and effectively reduced the redundant bands.The AUC of MTCEM algorithm reached the best value of 0.8785 on the band subset with 17 bands.This method could effectively reduce the data redundancy and had good detection effect on the chlamydospore targets,thus to provide new idea and technical support for the early detection of the mycogone perniciosa of Agaricus bisporus.
作者
邹金平
魏萱
ZOU Jin-ping;WEI Xuan(College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian350002, China;College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China)
出处
《福建农业科技》
CAS
2022年第2期1-8,共8页
Fujian Agricultural Science and Technology
基金
国家自然科学基金青年科学基金项目(61705037)。
关键词
高光谱
特征提取
孢子
检测
Hyperspectral
Feature extraction
Spores
Detection
