TY  - GEN
N2  - This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.
DO  - 10.1007/978-981-99-4973-1
DO  - doi
AB  - This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.
T1  - Deep learning for agricultural visual perception :crop pest and disease detection /
DA  - 2023.
CY  - Singapore :
AU  - Wang, Rujing.
AU  - Jiao, Lin.
AU  - Liu, Kang.
CN  - S494.5.D3
PB  - Springer,
PP  - Singapore :
PY  - 2023.
N1  - Backbone Network Based on Transformer
ID  - 1481050
KW  - Deep learning (Machine learning)
KW  - Artificial intelligence
SN  - 9789819949731
SN  - 9819949734
TI  - Deep learning for agricultural visual perception :crop pest and disease detection /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4973-1
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4973-1
ER  -