Chiharu Hongo

531 total citations
43 papers, 348 citations indexed

About

Chiharu Hongo is a scholar working on Ecology, Plant Science and Analytical Chemistry. According to data from OpenAlex, Chiharu Hongo has authored 43 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Ecology, 21 papers in Plant Science and 8 papers in Analytical Chemistry. Recurrent topics in Chiharu Hongo's work include Remote Sensing in Agriculture (21 papers), Smart Agriculture and AI (14 papers) and Spectroscopy and Chemometric Analyses (8 papers). Chiharu Hongo is often cited by papers focused on Remote Sensing in Agriculture (21 papers), Smart Agriculture and AI (14 papers) and Spectroscopy and Chemometric Analyses (8 papers). Chiharu Hongo collaborates with scholars based in Japan, Indonesia and United States. Chiharu Hongo's co-authors include I Wayan Nuarsa, Fumihiko Nishio, Hiroaki Kuze, Koki Homma, Masayasu Maki, Baba Barus, Daiki Saito, Hiroaki Shirakawa, Hiroyuki Wakabayashi and Tatsuhiko Shiraiwa and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Remote Sensing and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

In The Last Decade

Chiharu Hongo

40 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chiharu Hongo Japan 10 206 148 113 68 67 43 348
K. Colton Flynn United States 12 210 1.0× 108 0.7× 140 1.2× 103 1.5× 49 0.7× 44 429
Laigang Wang China 9 200 1.0× 179 1.2× 86 0.8× 113 1.7× 59 0.9× 30 353
Fernando Saragosa Rossi Brazil 11 177 0.9× 89 0.6× 205 1.8× 56 0.8× 55 0.8× 38 407
R. P. D. Ferraz Brazil 7 270 1.3× 105 0.7× 186 1.6× 104 1.5× 74 1.1× 20 428
Abhishek Danodia India 12 139 0.7× 164 1.1× 120 1.1× 63 0.9× 43 0.6× 34 341
Isabelle Piccard Belgium 8 308 1.5× 119 0.8× 145 1.3× 155 2.3× 96 1.4× 16 439
Chuanwen Wei China 9 240 1.2× 139 0.9× 84 0.7× 101 1.5× 69 1.0× 12 329
Vivek Naiken South Africa 7 157 0.8× 127 0.9× 95 0.8× 51 0.8× 34 0.5× 13 321
LeeAnn King United States 3 261 1.3× 89 0.6× 146 1.3× 105 1.5× 68 1.0× 5 313

Countries citing papers authored by Chiharu Hongo

Since Specialization
Citations

This map shows the geographic impact of Chiharu Hongo'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 Chiharu Hongo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chiharu Hongo more than expected).

Fields of papers citing papers by Chiharu Hongo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chiharu Hongo. 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 Chiharu Hongo. The network helps show where Chiharu Hongo may publish in the future.

Co-authorship network of co-authors of Chiharu Hongo

This figure shows the co-authorship network connecting the top 25 collaborators of Chiharu Hongo. A scholar is included among the top collaborators of Chiharu Hongo 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 Chiharu Hongo. Chiharu Hongo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Prasetyo, Lilik Budi, et al.. (2025). Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics. Smart Agricultural Technology. 10. 100766–100766. 2 indexed citations
2.
Junaedi, Ahmad, et al.. (2024). Evaluation Trial of Drought Damage of Rice Based on RGB Aerial Image by UAV. SHILAP Revista de lepidopterología. 44(4). 354–354. 1 indexed citations
3.
Hongo, Chiharu, et al.. (2024). Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data. Agronomy. 14(6). 1328–1328. 5 indexed citations
4.
Maki, Masayasu, et al.. (2023). Monitoring spatial and time-series variations in red crown rot damage of soybean in farmer fields based on UAV remote sensing. Plant Production Science. 26(1). 36–47. 16 indexed citations
5.
Lubis, Iskandar, Ahmad Junaedi, Bambang H. Trisasongko, et al.. (2022). Drought Damage Assessment for Crop Insurance Based on Vegetation Index by Unmanned Aerial Vehicle (UAV) Multispectral Images of Paddy Fields in Indonesia. Agriculture. 13(1). 113–113. 9 indexed citations
6.
Hongo, Chiharu, et al.. (2022). Advanced Damage Assessment Method for Bacterial Leaf Blight Disease in Rice by Integrating Remote Sensing Data for Agricultural Insurance. Journal of Agricultural Science. 14(4). 1–1. 4 indexed citations
7.
Hongo, Chiharu, et al.. (2021). Evaluating Multispectral Imaging for Assessing Bacterial Leaf Blight Damage in Indonesian Agricultural Insurance. SHILAP Revista de lepidopterología. 232. 3008–3008. 5 indexed citations
8.
Manago, Naohiro, et al.. (2020). Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia. Agriculture. 10(12). 625–625. 4 indexed citations
9.
Homma, Koki, et al.. (2020). Analysis of RGB Images to Estimate SPAD Values in Rice for UAV Remote Sensing. Japanese Journal of Crop Science. 89(1). 50–51. 1 indexed citations
10.
Hongo, Chiharu, et al.. (2020). Knowledge and Prevention of Farmer Households to the Japanese Encephalitis Infection in Badung Regency, Bali Province, Indonesia. Advances in Social Sciences Research Journal. 7(10). 37–48. 3 indexed citations
11.
Hongo, Chiharu, et al.. (2019). Relationships between Bacterial Leaf Blight and Other Diseases Based on Field Assessment in Indonesia. Tropical agriculture and development. 63(3). 113–121. 2 indexed citations
12.
Hongo, Chiharu, et al.. (2015). The effect of cultivation on changes in soil carbon stocks in the Andosols of Tokachi district, Hokkaido. Nihon Dojo Hiryogaku zasshi/Nippon dojō hiryōgaku zasshi. 86(6). 515–521. 1 indexed citations
13.
14.
Tanaka, Kenji, Keigo Noda, Kazuo Oki, et al.. (2013). Future Water Availability in the Asian Monsoon Region: A Case Study in Indonesia. 8(1). 25–31. 9 indexed citations
15.
Hongo, Chiharu, et al.. (2013). Yield Prediction of Sugar Beet Using Agricultural Spatial Information. Journal of the Japan Society for Precision Engineering. 79(11). 991–994. 1 indexed citations
16.
Nuarsa, I Wayan, et al.. (2012). Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia. International Journal of Remote Sensing. 33(17). 5402–5417. 49 indexed citations
17.
Nuarsa, I Wayan, Fumihiko Nishio, & Chiharu Hongo. (2011). Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data. Journal of Agricultural Science. 3(1). 29 indexed citations
18.
Nuarsa, I Wayan, Fumihiko Nishio, & Chiharu Hongo. (2011). Relationship between Rice Spectral and Rice Yield Using Modis Data. Journal of Agricultural Science. 3(2). 31 indexed citations
19.
Hongo, Chiharu, et al.. (2008). Effect of soil type on the time-course of changes in sugar beet (Beta vulgarisL.) productivity in Tokachi District, Hokkaido, Japan. Soil Science & Plant Nutrition. 54(6). 928–937. 7 indexed citations
20.
Suda, Masashi, et al.. (2005). Synergetic use of Landsat TM/SPOT HRVIR and IKONOS data for terrace rice fields monitoring. Journal of the Japan society of photogrammetry and remote sensing. 44(3). 37–45. 2 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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