文献类型：期刊文献

英文题名：Natural frequency identification model based on BP neural network for Camellia oleifera fruit harvesting

作者：Du, Xiaoqiang[1,2,3] Han, Xintao[1] Shen, Tengfei[1] Meng, Zhichao[1] Chen, Kaizhan[1] Yao, Xiaohua[4] Cao, Yongqing[4] Castro-Garcia, Sergio[5]

第一作者：Du, Xiaoqiang

通信作者：Du, XQ[1]

机构：[1]Zhejiang Sci Tech Univ, Sch Mech Engn, Hangzhou 310018, Peoples R China;[2]Key Lab Zhejiang Transplanting Equipment Technol, Hangzhou 310018, Peoples R China;[3]Collaborat Innovat Ctr Intelligent Prod Equipment, Hangzhou 310018, Peoples R China;[4]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou 311400, Peoples R China;[5]Univ Cordoba, Dept Rural Engn, ETSI Agronomos & Montes, Campus Rabanales,Ctra Nacl 4 Km 396, Cordoba, Spain

年份：2024

卷号：237

起止页码：38-49

外文期刊名：BIOSYSTEMS ENGINEERING

收录：;EI(收录号：20234915179860);Scopus(收录号：2-s2.0-85178500055);WOS:【SCI-EXPANDED(收录号:WOS:001137377900001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 31971798) , the National Key Research and Development Program of China (Grant No. 2022YFD22021) , the Zhejiang Provincial Key Research & Development Program (Grant No. 2022C02057) , the SNJF Science and Technology Collaborative Program of Zhejiang Province (Grant No. 2022SNJF017) , the Scientific Research Fund of Zhejiang Provincial Education Department (Grant No. Y202250713) , the 521 Talent Plan of Zhejiang Sci-Tech University, and the Cultivation Project for Youth Discipline Leader in Zhejiang Provincial Institute.

语种：英文

外文关键词：Camellia oleifera tree; Vibratory harvesting; Natural frequency; BP neural network; Identification model

摘要：Vibratory harvesting is an important means of mechanically harvesting tree fruit. The optimal excitation parameters are usually experimentally determined under complex conditions with different environments and machine configurations. Optimisation methods include tree modelling and dynamic analysis but experimental validation can take much time due to the complexity of tree structure and properties. A simple and appropriate identification model that could identify the natural frequencies of trees might simplify the process and promote the technology. A natural frequency identification model is proposed based on back propagation (BP) neural network to identifying the natural frequency of the tree based on its structure. Taking Camellia oleifera tree with its upright canopy as an example, the excitation parameters that can achieve better harvesting of fruit was determined here by orthogonal test. A dynamic model was established, and the tree structure variables were derived as the input layer of the model. The dataset of tree dynamics was established by finite element analysis and the effective natural frequency region was set as the model output layer. A natural frequency identification model was established based on TensorFlow, where the input and output parameters are fitted using a BP neural network. Application of the model was carried out after substantial training and testing. In the range of natural frequencies 6-7Hz, the mean square error between the natural frequency identification value and the measured value was only 0.0408, which verified the reliability of the model.