Abstract:To solve the problems of low efficiency and inaccurate detection results of traditional grain storage monitoring, a realtime monitoring and prewarning system (RMPS) was proposed based on deep learning. The largescale samples of several pests of sitophilus oryzae linne, cryptolestes ferrugineus and tribolium castaneum in grain depot were collected, and the samples were learned and trained by the CNN to construct a neural network model. The internal image information of grain store was collected by the new collector in real time to detect the pest species and probability. The monitoring results were published to mobile phone in the form of web. A small simulated granary with RMPS was built under laboratory conditions. The simulation results show that the RMPS can change the monitoring mode from the traditional fixedpoint timing monitoring to the realtime monitoring with improved pest detection accuracy rate over 90%. The specially designed collector and the mobile client can make the deployment of RMPS more simple and convenient, which achieves high practicability and scalability.
罗强, 黄睿岚, 朱轶. 基于深度学习的粮库虫害实时监测预警系统[J]. 江苏大学学报(自然科学版), 2019, 40(2): 203-208.
LUO Qiang, HUANG Rui-Lan, ZHU Yi. Realtime monitoring and prewarning system for grain
storehouse pests based on deep learning[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2019, 40(2): 203-208.