IRB-5-CA Net: A Lightweight, Deep Learning-Based Approach to Wheat Seed Identification
Author:
Affiliation:
1. College of Information Sciences and Technology, Gansu Agricultural University, Lanzhou, China
2. Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
Funder
National Natural Science Foundation of China
Colleges and Universities in Gansu of China
Industrial Support and Guidance Project of Universities in Gansu Province, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10292649.pdf?arnumber=10292649
Reference28 articles.
1. Image recognition model of light VGG maize kernel based on attention mechanism;mengyan;Journal of the Chinese Cereals and Oils Association,2023
2. Lightweight Corn Seed Disease Identification Method Based on Improved ShuffleNetV2
3. Identification of crop leaf diseases based on improved MobileNetV2 model;huanxin;Henan J Agr Sci,2023
4. Model for the recognition of large-scale multi-class diseases and pests;changji;Trans Chin Soc Agricult Eng,2022
5. Computer-vision classification of corn seed varieties using deep convolutional neural network
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