Saeed Khaki

717 total citations · 1 hit paper
8 papers, 396 citations indexed

About

Saeed Khaki is a scholar working on Plant Science, Ecology and Analytical Chemistry. According to data from OpenAlex, Saeed Khaki has authored 8 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Plant Science, 4 papers in Ecology and 4 papers in Analytical Chemistry. Recurrent topics in Saeed Khaki's work include Smart Agriculture and AI (4 papers), Spectroscopy and Chemometric Analyses (4 papers) and Remote Sensing in Agriculture (4 papers). Saeed Khaki is often cited by papers focused on Smart Agriculture and AI (4 papers), Spectroscopy and Chemometric Analyses (4 papers) and Remote Sensing in Agriculture (4 papers). Saeed Khaki collaborates with scholars based in United States, China and Germany. Saeed Khaki's co-authors include Hieu Pham, Lizhi Wang, Lizhi Wang, Wenzhi Zeng, Gina Lopez, Thomas Gaiser, Jaber Rahimi, Amit Kumar Srivastava, Frank Ewert and Yang Liu and has published in prestigious journals such as PLoS ONE, Scientific Reports and Neurocomputing.

In The Last Decade

Saeed Khaki

6 papers receiving 383 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.

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data

2022 103 citations Amit Kumar Srivastava, Saeed Khaki et al. Scientific Reports profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saeed Khaki United States	6	296	181	81	50	39	8	396
Tianle Yang China	12	245 0.8×	170 0.9×	95 1.2×	63 1.3×	16 0.4×	29	388
Gercina Gonçalves da Silva Brazil	6	572 1.9×	220 1.2×	168 2.1×	66 1.3×	22 0.6×	9	720
Hongbao Ye China	10	243 0.8×	102 0.6×	71 0.9×	38 0.8×	45 1.2×	24	356
Ziyuan Hao China	10	252 0.9×	103 0.6×	80 1.0×	31 0.6×	21 0.5×	32	428
Ran Nisim Lati Israel	13	394 1.3×	174 1.0×	38 0.5×	100 2.0×	29 0.7×	49	513
Anna Kicherer Germany	15	576 1.9×	237 1.3×	113 1.4×	101 2.0×	12 0.3×	32	658
Hema Duddu Canada	12	233 0.8×	127 0.7×	46 0.6×	59 1.2×	18 0.5×	29	318
Sourav Bhadra United States	9	350 1.2×	328 1.8×	188 2.3×	94 1.9×	21 0.5×	16	584
Feiyu Zhu United States	12	309 1.0×	103 0.6×	47 0.6×	89 1.8×	11 0.3×	27	439
Yogesh Anand Rajwade India	13	380 1.3×	131 0.7×	71 0.9×	50 1.0×	30 0.8×	34	514

Countries citing papers authored by Saeed Khaki

Since Specialization

Citations

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

Fields of papers citing papers by Saeed Khaki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saeed Khaki

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

All Works

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8 of 8 papers shown

1.

Khaki, Saeed, et al.. (2024). RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models. 1665–1680.

2.

Khaki, Saeed, et al.. (2024). A hybrid deep learning-based approach for optimal genotype by environment selection. Frontiers in Artificial Intelligence. 7. 1312115–1312115.

3.

Khaki, Saeed, et al.. (2022). High-throughput image-based plant stand count estimation using convolutional neural networks. PLoS ONE. 17(7). e0268762–e0268762. 8 indexed citations

4.

Srivastava, Amit Kumar, Saeed Khaki, Gina Lopez, et al.. (2022). Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Scientific Reports. 12(1). 3215–3215. 103 indexed citations breakdown →

5.

Khaki, Saeed, et al.. (2022). WheatNet: A lightweight convolutional neural network for high-throughput image-based wheat head detection and counting. Neurocomputing. 489. 78–89. 74 indexed citations

6.

Khaki, Saeed, Hieu Pham, & Lizhi Wang. (2021). Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Scientific Reports. 11(1). 11132–11132. 119 indexed citations

7.

Khaki, Saeed, et al.. (2021). DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation. Knowledge-Based Systems. 218. 106874–106874. 59 indexed citations

8.

Khaki, Saeed, et al.. (2020). Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE. 15(5). e0233382–e0233382. 33 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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