Chad DeChant

1.2k total citations · 1 hit paper
13 papers, 868 citations indexed

About

Chad DeChant is a scholar working on Plant Science, Ecology and Artificial Intelligence. According to data from OpenAlex, Chad DeChant has authored 13 papers receiving a total of 868 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Plant Science, 4 papers in Ecology and 3 papers in Artificial Intelligence. Recurrent topics in Chad DeChant's work include Smart Agriculture and AI (5 papers), Remote Sensing in Agriculture (4 papers) and Natural Language Processing Techniques (2 papers). Chad DeChant is often cited by papers focused on Smart Agriculture and AI (5 papers), Remote Sensing in Agriculture (4 papers) and Natural Language Processing Techniques (2 papers). Chad DeChant collaborates with scholars based in United States, Canada and Germany. Chad DeChant's co-authors include Hod Lipson, Tyr Wiesner‐Hanks, Rebecca Nelson, Ethan L. Stewart, Michael A. Gore, Nicholas Kaczmar, Harvey Wu, Siyuan Chen, Jason Yosinski and Jacob Varley and has published in prestigious journals such as Frontiers in Plant Science, Remote Sensing and Phytopathology.

In The Last Decade

Chad DeChant

11 papers receiving 832 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.

Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning

2017 305 citations Chad DeChant, Tyr Wiesner‐Hanks et al. Phytopathology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chad DeChant United States	7	623	232	230	102	83	13	868
Javier Ruiz‐Hidalgo Spain	14	441 0.7×	120 0.5×	137 0.6×	253 2.5×	41 0.5×	44	834
Xinhua Wei China	13	601 1.0×	133 0.6×	178 0.8×	88 0.9×	75 0.9×	31	915
Juncheng Ma China	16	916 1.5×	346 1.5×	333 1.4×	109 1.1×	23 0.3×	46	1.2k
Wenxia Bao China	15	412 0.7×	156 0.7×	139 0.6×	135 1.3×	19 0.2×	55	698
Guichao Lin China	12	794 1.3×	188 0.8×	103 0.4×	159 1.6×	55 0.7×	22	1.0k
Mohd Shahrimie Mohd Asaari Malaysia	11	342 0.5×	334 1.4×	303 1.3×	200 2.0×	21 0.3×	27	943
Shangpeng Sun China	15	605 1.0×	93 0.4×	383 1.7×	63 0.6×	29 0.3×	43	916
Mark Whitty Australia	15	485 0.8×	92 0.4×	219 1.0×	146 1.4×	103 1.2×	41	816
Chunlong Zhang China	17	506 0.8×	108 0.5×	156 0.7×	125 1.2×	23 0.3×	38	754

Countries citing papers authored by Chad DeChant

Since Specialization

Citations

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

Fields of papers citing papers by Chad DeChant

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chad DeChant

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

All Works

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13 of 13 papers shown

1.

DeChant, Chad. (2025). Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated. 321–332. 1 indexed citations

2.

DeChant, Chad, et al.. (2025). Episodic Memory Verbalization Using Hierarchical Representations of Life-Long Robot Experience. 783–790.

3.

DeChant, Chad, et al.. (2023). Learning to summarize and answer questions about a virtual robot’s past actions. Autonomous Robots. 47(8). 1103–1118. 7 indexed citations

4.

DeChant, Chad, et al.. (2021). Toward robots that learn to summarize their actions in natural language: a set of tasks. 2 indexed citations

5.

Wiesner‐Hanks, Tyr, Harvey Wu, Ethan L. Stewart, et al.. (2019). Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data. Frontiers in Plant Science. 10. 1550–1550. 75 indexed citations

6.

Parson, Edward A., et al.. (2019). Could AI Drive Transformative Social Progress? What Would This Require?. SSRN Electronic Journal.

7.

Stewart, Ethan L., Tyr Wiesner‐Hanks, Nicholas Kaczmar, et al.. (2019). Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sensing. 11(19). 2209–2209. 89 indexed citations

8.

Wu, Harvey, Tyr Wiesner‐Hanks, Ethan L. Stewart, et al.. (2019). Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery. 2(1). 1–9. 87 indexed citations

9.

DeChant, Chad, et al.. (2019). Predicting the accuracy of neural networks from final and intermediate layer outputs. 1 indexed citations

10.

Jordan, Sara R., et al.. (2019). Creating a Tool to Reproducibly Estimate the Ethical Impact of Artificial Intelligence. eScholarship (California Digital Library). 1 indexed citations

11.

Wiesner‐Hanks, Tyr, Ethan L. Stewart, Nicholas Kaczmar, et al.. (2018). Image set for deep learning: field images of maize annotated with disease symptoms. BMC Research Notes. 11(1). 440–440. 122 indexed citations

12.

DeChant, Chad, Tyr Wiesner‐Hanks, Siyuan Chen, et al.. (2017). Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. Phytopathology. 107(11). 1426–1432. 305 indexed citations breakdown →

13.

Varley, Jacob, et al.. (2017). Shape completion enabled robotic grasping. 2442–2447. 178 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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