Capsicum Recognition Based on Python and Convolutional Neural Networks
Main Article Content
Abstract
This paper first briefly introduces the Python language and TensorFlow architecture, normalizes, calibrates, and divides the collected images, and then analyzes the processing process of the AlexNet model, adopts the VGG model and causes the data of the transfer learning method to be identified, to get 90% accuracy, using cross-entropy training loss function, the loss value is 0.3.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
XING Zenong, XIN Xin, LIU Yazhong, LI Qiuxiao, ZHANG Jiayuan Current situation and prospect of mechanization of chili pepper industry in China[J]. Bulletin of Agricultural Science and Technology, 2021(06): 229-230+259
SU Anjin,DU Chan,DU Juan. Development and test of self-propelled pepper harvesting machinery[J]. Agricultural Engineering,2021,11(06):17-19
WU Liang. Design and analysis of small walk-behind pepper harvester[D].Chengdu University, 2021. DOI:10.27917/d. cnki.gcxdy.2021.000207.
XU Dongsheng. Design and research of line pepper harvester[D].Gansu Agricultural University,2017.)
FU Longsheng,FENG Yali,ELKAMIL Tola,LIU Zhihao,LI Rui,CUI Yongjie. Image recognition method of multi-cluster kiwifruit in field based on convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(02):205-211.)
LI Huaitao,CAO Zhongda,ZHU Chenghui,CHEN Keqiong,WANG Jianping,LIU Xuejing,ZHENG Chengqiang. Cognitive method of intelligent feedback of green plum grade based on deep ensemble learning[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(23):276-283.)
DUAN Lingfeng,XIONG Xiong,LIU Qian,YANG Wanneng,HUANG Chenglong. Field rice ear segmentation based on deep fully convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(12):202-209.)
YANG Yang,ZHANG Yalan,MIAO Wei,ZHANG Tie,CHEN Liqing,HUANG Lili. Research on accurate identification and localization of maize rhizome based on convolutional neural network[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):46-53.)
MA Juncheng, DU Keming, ZHENG Feixiang, ZHANG Lingling, SUN Zhongfu Greenhouse cucumber disease identification system based on convolutional neural network[J] Transactions of the Chinese Society of Agricultural Engineering,2018,34(12):186-192
LIU Deying,WANG Jialiang,LIN Xiangze,CHEN Jing,YU Haiming. Identification method of white-backed planthopper based on convolutional neural network[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(05):51-56.)
LONG Mansheng,OUYANG Chunjuan,LIU Huan,FU Qing. Image recognition of Camellia oleifera disease based on convolutional neural network and transfer learning[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(18):194-201.)
YE Jianlong, HU Xinhai Research on image recognition algorithm based on convolutional neural network[J]. Journal of Anyang Normal University,2021(05):14-18.DOI:10.16140/j.cnki.1671-5330. 202 1.05. 005
Y. LeCun,L. Bottou,Y. Bengio,P. Haffner. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE,1998,86(11).
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM,2017,60(6).
Karen Simonyan,Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. [J]. CoRR,2014,abs/1409.1556.
WU Jingzhu,LI Xiaoqi,LIN Long,LIU Cuiling,LIU Zhi,YUAN Yuwei Hyperspectral rapid discrimination of rice origin based on AlexNet convolutional neural network[J]. Journal of Chinese Journal of Food Science,2022,22(01):282-288.DOI:10.16429/j.1009-7848.2022.01.030