典型文献
Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism
文献摘要：
In the actual complex environment, the recognition accuracy of crop leaf disease is often not high. Inspired by the brain parallel interaction mechanism, a two-stream parallel interactive convolutional neural network ( TSPI-CNN ) is proposed to improve the recognition accuracy. TSPI-CNN includes a two-stream parallel network ( TSP-Net) and a parallel interactive network ( PI-Net) . TSP-Net simulates the ventral and dorsal stream. PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission. A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2, PlantVillage, Apple-3 leaf, and Cassava leaf datasets. Furthermore, the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed. The experimental results show that as the number of interactions increases, the recognition accuracy of the network also increases. Finally, the network is visualized to show the working mechanism of the network and provide enlightenment for future research.
文献关键词：
中图分类号：
作者姓名：
YUAN Hui;HAO Kuangrong;WEI Bing
作者机构：
Engineering Research Center of Digitized Textile&Apparel Technology,Ministry of Education,College of Information Science and Technology,Donghua University,Shanghai 201620,China
文献出处：
引用格式：
[1]YUAN Hui;HAO Kuangrong;WEI Bing-.Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism)[J].东华大学学报（英文版）,2022(02):146-155
A类：
TSPI,Cassava
B类：
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AB值：
0.498491
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
