Open AccessResearch of OpenMV Intelligent Monitoring and Disease Identification System
Author(s) -
Xiuqing Wang,
Qi Chen,
Shi-jia Yao,
Xiao-yun Jia
Publication year - 2019
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 - 10.1088/1742-6596/1267/1/012032
Subject(s) - cuckoo search , classifier (uml) , identification (biology) , computer science , artificial intelligence , artificial neural network , pattern recognition (psychology) , real time computing , machine learning , botany , particle swarm optimization , biology
Aiming at the requirement of video surveillance and disease identification in modern greenhouse, this paper designed a system consisted by greenhouse intelligent monitoring and disease identification system. At first, this paper using OpenMV camera designed a disease monitoring system which could recognize and collect disease images automatically by multi-color thresholds tracking. Then, this paper designed a cuckoo search and BP neural network collaborative search (CSBP-CS) algorithm, the algorithm combined the global search capability of Cuckoo Search(CS) and back-propagation algorithm of BP algorithm to optimize weights and thresholds collaboratively. this paper took three tomato diseases and normal leaves as research objects, firstly step was to separate the disease spots from disease images, then was to extract 56 classification features and select 47 excellent classification features by relief F to construct CSBP-CS RF classifier. Finally, this paper compared the classification accuracy of CSBP-CS with CSBP-CS RF network and analyzed the effectiveness of relief F. The simulation results showed that the average correct recognition rate of the CSBP-CS RF was approximately equal to CSBP-CS under the same conditions, but CSBP-CS RF is more simple, so Relief F can help to speed up the efficiency of CSBP-CS.