TY - GEN
T1 - A multi-organ plant identification method using convolutional neural networks
AU - Guo, Pei
AU - Gao, Qiang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Most of the existing studies for plant identification focus on identifying the plants with pictures of a single organ, such as leaf or flower. The information provided by single organ is always limited and sometimes confused. A new identification method, Multi-Organ Integrated PlantNet, is proposed in this paper which combines the diverse information of multiple organs. The method consists of two stages. Firstly, we use the framework of convolutional neural networks (CNN) to make the preliminary plant decision based on single organ. Then we make the final plant decision based on multiple organs using Linear Weighted Classification and Support Vector Machine (SVM). The experiments conducted on the dataset of 100 species show that our new method improve the identification accuracy by about 15% compared with the Single Organ PlantNet.
AB - Most of the existing studies for plant identification focus on identifying the plants with pictures of a single organ, such as leaf or flower. The information provided by single organ is always limited and sometimes confused. A new identification method, Multi-Organ Integrated PlantNet, is proposed in this paper which combines the diverse information of multiple organs. The method consists of two stages. Firstly, we use the framework of convolutional neural networks (CNN) to make the preliminary plant decision based on single organ. Then we make the final plant decision based on multiple organs using Linear Weighted Classification and Support Vector Machine (SVM). The experiments conducted on the dataset of 100 species show that our new method improve the identification accuracy by about 15% compared with the Single Organ PlantNet.
KW - convolutional neural networks (CNN)
KW - multi-organ
KW - plant identification
KW - support vector machine (SVM)
UR - https://www.scopus.com/pages/publications/85047021690
U2 - 10.1109/ICSESS.2017.8342935
DO - 10.1109/ICSESS.2017.8342935
M3 - 会议稿件
AN - SCOPUS:85047021690
T3 - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
SP - 371
EP - 376
BT - ICSESS 2017 - Proceedings of 2017 IEEE 8th International Conference on Software Engineering and Service Science
A2 - Wenzheng, Li
A2 - Babu, M. Surendra Prasad
A2 - Xiaohui, Lei
PB - IEEE Computer Society
T2 - 8th IEEE International Conference on Software Engineering and Service Science, ICSESS 2017
Y2 - 24 November 2017 through 26 November 2017
ER -