Agronomy

JournalCitation impact & published research

Agronomy is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 19,968 works, 329,750 citations, an h-index of 166 and an APC (USD) of 2,450.

19,968

Works

329,750

Citations

166

h-index

2,450

APC (USD)

IDs:OpenAlex

What are the most-cited papers on Agronomy?

ScholarIQmost cited works

Development and Use of Simple Sequence Repeats (SSRs) Markers for Sugarcane Breeding and Genetic Studies

Ali Ahmad, Jinda Wang, Yong‐Bao Pan, Rahat Sharif, San‐Ji Gao

Agronomy. 201868 CitationsOPEN ACCESS

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Rama Gautam, Fahad Alblehai, Magdy Elghatwary, Aman Singh

Agronomy. 202353 CitationsOPEN ACCESS

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif

Agronomy. 202245 CitationsOPEN ACCESS

Antifungal Activity of Essential Oil and Main Components from Mentha pulegium Growing Wild on the Chilean Central Coast

Iván Montenegro, Bastián Said, Patrício Godoy, Ximena Besoaín, Carol Parra, Katy Díaz, Alejandro Madrid

Agronomy. 202040 CitationsOPEN ACCESS

Biochar Amendment Suppressed Fusarium Wilt and Altered the Rhizosphere Microbial Composition of Tomatoes

Xue Jin, Xingang Zhou, Fengzhi Wu, Wensheng Xiang, Kai Pan

Agronomy. 202325 CitationsOPEN ACCESS

Related on ScholarIQ

Development and Use of Simple Sequence Repeats (SSRs) Markers for Sugarcane Breeding and Genetic Studies

Paper

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Paper

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

Paper

Antifungal Activity of Essential Oil and Main Components from Mentha pulegium Growing Wild on the Chilean Central Coast

Paper

Biochar Amendment Suppressed Fusarium Wilt and Altered the Rhizosphere Microbial Composition of Tomatoes

Paper

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

Paper