Jingye Han

843 total citations · 1 hit paper
16 papers, 666 citations indexed

About

Jingye Han is a scholar working on Ecology, Plant Science and Global and Planetary Change. According to data from OpenAlex, Jingye Han has authored 16 papers receiving a total of 666 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Ecology, 8 papers in Plant Science and 4 papers in Global and Planetary Change. Recurrent topics in Jingye Han's work include Remote Sensing in Agriculture (8 papers), Smart Agriculture and AI (6 papers) and Climate change impacts on agriculture (3 papers). Jingye Han is often cited by papers focused on Remote Sensing in Agriculture (8 papers), Smart Agriculture and AI (6 papers) and Climate change impacts on agriculture (3 papers). Jingye Han collaborates with scholars based in China and United States. Jingye Han's co-authors include Qi Yang, Liangsheng Shi, Yuanyuan Zha, Penghui Zhu, Jin Yu, Kai Huang, Yongwen Ma, Jinquan Wan, Zhuowei Chen and Yan Wang and has published in prestigious journals such as Optics Express, Sensors and Applied Surface Science.

In The Last Decade

Jingye Han

16 papers receiving 654 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images

2019 289 citations Qi Yang, Liangsheng Shi et al. Field Crops Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jingye Han China	10	379	359	161	88	79	16	666
Sheng Chang China	14	300 0.8×	193 0.5×	133 0.8×	68 0.8×	209 2.6×	38	1.1k
Jingyi Jiang China	15	359 0.9×	236 0.7×	240 1.5×	34 0.4×	191 2.4×	59	691
Yijian Li China	14	410 1.1×	337 0.9×	221 1.4×	163 1.9×	94 1.2×	30	919
Ying Quan China	16	104 0.3×	210 0.6×	193 1.2×	78 0.9×	133 1.7×	65	912
Kaiyuan Li China	13	196 0.5×	146 0.4×	95 0.6×	63 0.7×	102 1.3×	43	667
Sujay Kumar Dutta India	12	130 0.3×	108 0.3×	43 0.3×	40 0.5×	64 0.8×	39	458
Kaihua Wu China	12	350 0.9×	485 1.4×	84 0.5×	262 3.0×	51 0.6×	56	840
Mohammad Reza Mobasheri Iran	12	310 0.8×	353 1.0×	172 1.1×	236 2.7×	91 1.2×	56	785
Tianyue Xu China	10	213 0.6×	125 0.3×	97 0.6×	51 0.6×	57 0.7×	17	344
Muhammad Naveed Tahir Pakistan	15	150 0.4×	303 0.8×	82 0.5×	69 0.8×	120 1.5×	45	613

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-authorship network of co-authors of Jingye Han

This figure shows the co-authorship network connecting the top 25 collaborators of Jingye Han. A scholar is included among the top collaborators of Jingye Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jingye Han. Jingye Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Han, Jingye, Liangsheng Shi, Qi Yang, Yu Jin, & Ioannis N. Athanasiadis. (2025). Knowledge-guided machine learning with multivariate sparse data for crop growth modelling. Field Crops Research. 328. 109912–109912. 1 indexed citations

Li, Jinmin, et al.. (2024). Improving Simulations of Rice Growth and Nitrogen Dynamics by Assimilating Multivariable Observations into ORYZA2000 Model. Agronomy. 14(10). 2402–2402. 2 indexed citations

Li, Jinmin, et al.. (2024). Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images. Computers and Electronics in Agriculture. 227. 109497–109497. 2 indexed citations

Li, Mingjing, et al.. (2023). An Improved Lightweight Dense Pedestrian Detection Algorithm. Applied Sciences. 13(15). 8757–8757. 10 indexed citations

Li, Mingjing, et al.. (2023). Peripheral Blood Leukocyte Detection Based on an Improved Detection Transformer Algorithm. Sensors. 23(16). 7226–7226. 2 indexed citations

Yang, Qi, Liangsheng Shi, Jingye Han, et al.. (2023). Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity. Field Crops Research. 293. 108847–108847. 11 indexed citations

Han, Jingye, Liangsheng Shi, Qi Yang, et al.. (2022). Rice yield estimation using a CNN-based image-driven data assimilation framework. Field Crops Research. 288. 108693–108693. 30 indexed citations

Yang, Qi, Liangsheng Shi, Jingye Han, Zhuowei Chen, & Jin Yu. (2021). A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images. Field Crops Research. 277. 108419–108419. 36 indexed citations

Yu, Jin, Liangsheng Shi, Jingye Han, et al.. (2021). Assessing parametric and nitrogen fertilizer input uncertainties in the ORYZA_V3 model predictions. Agronomy Journal. 113(6). 4965–4981. 5 indexed citations

10.

Han, Jingye, Liangsheng Shi, Qi Yang, et al.. (2020). Real-time detection of rice phenology through convolutional neural network using handheld camera images. Precision Agriculture. 22(1). 154–178. 41 indexed citations

11.

Wang, Yan, et al.. (2020). Enhancing water stability of MIL-101(Cr) by doping Ni(II). Applied Surface Science. 525. 146511–146511. 63 indexed citations

12.

Yang, Qi, Liangsheng Shi, Jingye Han, Jin Yu, & Kai Huang. (2020). A near real-time deep learning approach for detecting rice phenology based on UAV images. Agricultural and Forest Meteorology. 287. 107938–107938. 124 indexed citations

13.

Han, Jingye, Yan Wang, Jinquan Wan, & Yongwen Ma. (2019). Catalytic hydrolysis of cellulose by phosphotungstic acid–supported functionalized metal-organic frameworks with different electronegative groups. Environmental Science and Pollution Research. 26(15). 15345–15353. 18 indexed citations

14.

Yang, Qi, Liangsheng Shi, Jingye Han, Yuanyuan Zha, & Penghui Zhu. (2019). Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Research. 235. 142–153. 289 indexed citations breakdown →

15.

Song, Xiangrong, et al.. (2014). Milk composition, milk consumption, and growth rate of a captive spotted seal (Phoca largha) pup from Liaodong Bay, China. Canadian Journal of Zoology. 92(5). 449–452. 5 indexed citations

16.

Han, Jingye, et al.. (2005). Optical nonlinearity of ZnO microcrystallite enhanced by interfacial state. Optics Express. 13(23). 9211–9211. 27 indexed citations

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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