Computers and Electronics in Agriculture

JournalCitation impact & published research

Computers and Electronics in Agriculture is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 11,071 works, 437,084 citations, an h-index of 221 and an APC (USD) of 3,680.

11,071

Works

437,084

Citations

221

h-index

3,680

APC (USD)

IDs:OpenAlex

What are the most-cited papers on Computers and Electronics in Agriculture?

ScholarIQmost cited works

Using deep transfer learning for image-based plant disease identification

Junde Chen, Jinxiu Chen, Defu Zhang, Yuandong Sun, Yaser A. Nanehkaran

Computers and Electronics in Agriculture. 20201,023 Citations

Monitoring plant diseases and pests through remote sensing technology: A review

Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo González‐Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang

Computers and Electronics in Agriculture. 2019618 Citations

Deep feature based rice leaf disease identification using support vector machine

Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, Santi Kumari Behera

Computers and Electronics in Agriculture. 2020587 Citations

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Artzai Picón, Aitor Álvarez-Gila, Maximiliam Seitz, Amaia Ortiz‐Barredo, Jone Echazarra, Alexander Johannes

Computers and Electronics in Agriculture. 2018558 Citations

A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun

Computers and Electronics in Agriculture. 2018558 Citations

Related on ScholarIQ

Using deep transfer learning for image-based plant disease identification

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Monitoring plant diseases and pests through remote sensing technology: A review

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Deep feature based rice leaf disease identification using support vector machine

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Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

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A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

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An automated detection and classification of citrus plant diseases using image processing techniques: A review

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