Jingye Han

868 citations
16 papers · 677 · 1 hit paper · h-index 10

Impact in

Papers in

    • Remote Sensing in Agriculture 8
    • Smart Agriculture and AI 6
    • Rice Cultivation and Yield Improvement 3

Jingye Han

16 papers receiving 670 citations

Jingye Han's Hit Papers

Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images 2019 · 292 citations
2920+2+4Years since publication50100150200250

Peers

Jingye Han
Comparison fields: 5 of 77
  • Ecology 382
  • Environmental Engineering 161
  • Plant Science 360
  • Analytical Chemistry 90
  • Ecological Modeling 20
Replace Sheng Chang with:
Sheng Chang China
Jingyi Jiang China
Yijian Li China
Danielle Elis Garcia Furuya Brazil
Marcelo Theophilo Folhes Brazil
Mauro dos Santos de Arruda Brazil
Sourav Bhadra United States
Ying Quan China
Nik Norasma Che’Ya Malaysia
Changwei Tan China
Jingye Han relative to Sheng Chang China Sheng Chang's profile →
Citations per field
00.5×3.3×
Sheng Chang · 1×
Citations per year

Countries citing papers authored by Jingye Han

Since Specialization
Citations

This map shows the geographic impact of Jingye Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jingye Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingye Han more than expected).

Fields of papers citing papers by Jingye Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jingye Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jingye Han. The network helps show where Jingye Han may publish in the future.

Co-authors

The 25 scholars most cited alongside Jingye Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jingye Han Line = papers co-authored together Jingye Han links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images
Hit paper breakdown →
2019292
2 2020128
3 202065
4 202042
5 202136
6 202230
7 200527
8 201918
9 202311
10 202310
11 20145
12 20215
13 20242
14 20232
15 20252
16 20242

About Jingye Han

Jingye Han is a scholar working on Ecology, Plant Science, Global and Planetary Change, Ecology, Evolution, Behavior and Systematics and Analytical Chemistry, having authored 16 papers that have together received 677 indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (8 papers), Smart Agriculture and AI (6 papers), Plant Water Relations and Carbon Dynamics (3 papers), Spectroscopy and Chemometric Analyses (3 papers), Rice Cultivation and Yield Improvement (3 papers), Climate change impacts on agriculture (3 papers), Metal-Organic Frameworks: Synthesis and Applications (2 papers) and Remote Sensing and LiDAR Applications (2 papers). The work is most often cited by research in Ecology (382 citations), Environmental Engineering (161 citations), Plant Science (360 citations), Analytical Chemistry (90 citations) and Ecological Modeling (20 citations). Jingye Han has collaborated with scholars based in China, Netherlands and United States. Frequent co-authors include Liangsheng Shi, Qi Yang, Yuanyuan Zha, Penghui Zhu, Jin Yu, Kai Huang, Yongwen Ma, Jinquan Wan, Zhuowei Chen and Yan Wang. Their work appears in journals such as Field Crops Research, Canadian Journal of Zoology, Agronomy Journal, Applied Sciences and Optics Express.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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