Open AccessResearch on Crop Disease Classification Algorithm Based on Mixed Attention Mechanism
Author(s) -
Zhe Chen,
Xingzhen Bai,
Peng Ji,
Fengying Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1961/1/012048
Subject(s) - computer science , identification (biology) , mechanism (biology) , set (abstract data type) , artificial neural network , data mining , stability (learning theory) , artificial intelligence , machine learning , measure (data warehouse) , pattern recognition (psychology) , algorithm , philosophy , botany , epistemology , biology , programming language
The prevention and control of crop diseases is an important measure to ensure the yield of crops, and the prerequisite for achieving this link is to improve the accuracy of crop disease identification. This paper constructs a new hybrid attention mechanism by realizing the serial connection of Spatial attention and Efficient Channel Attention (ECA)and proposes a new hybrid attention module Spatial_Efficient Channel Attention (S_ECA). This module is combined with the neural network to improve the disease classification and recognition ability of the network model, the network stability, and the overall performance of the network. In the classification of a part of the data of the crop disease data set in the 2018AI_Chllenger competition, a classification accuracy of 87.28% was achieved, which is an increase of 0.78% compared to the original network, thus verifying the effectiveness of the algorithm in this paper.