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About the database ScholarIQanswers from OpenAlex & ORCID
How has Huiru Zhou's publication output changed over time?
ScholarIQpublication output · 2022–2026
Output grew100% over the shown period — from 2 works in 2022 to 4 in 2026.
2
2
2
4
2022202420252026
What are the most-cited papers on Huiru Zhou?
ScholarIQmost cited works
Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs
Jie Deng, Huiru Zhou, Xuan Lv, Lujia Yang, Jiali Shang, Qiuyu Sun, Xin Zheng, Congying Zhou, Baoqiang Zhao, Jiachong Wu, Zhanhong Ma
Computers and Electronics in Agriculture. 202253 Citations
Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis
Huiru Zhou, Qiang Lai, Qiong Huang, Dingzhou Cai, Dong Huang, Bo-Ming Wu
Agriculture. 202414 CitationsOPEN ACCESS
Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network
Huiru Zhou, Jie Deng, Dingzhou Cai, Xuan Lv, Bo Wu
Frontiers in Plant Science. 202211 CitationsOPEN ACCESS
Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks
Huiru Zhou, Dingzhou Cai, Lijie Lin, Dong Huang, Bo-Ming Wu
Smart Agricultural Technology. 20254 CitationsOPEN ACCESS
Deep recognition of rice disease images: how many training samples do we really need?
Huiru Zhou, Dong Huang, B. M. Wu
S206298197. 20241 Citations
Related on ScholarIQ
South China Agricultural University
Institution
Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs
Paper
Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis
Paper
Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network
Paper
Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks
Paper
Deep recognition of rice disease images: how many training samples do we really need?
Paper