Computers and Electronics in Agriculture

Aims & Scope

Computers and Electronics in Agriculture provides international coverage of advances in the development and application of computer hardware, software, electronic instrumentation, and control systems for solving problems in agriculture, including agronomy, horticulture (in both its food and amenity aspects), forestry, aquaculture, and animal/livestock farming.

The journal publishes original papers, reviews, and applications notes on topics pertaining to advances in the use of computers or electronics in plant or animal agricultural production, including agricultural soils, water, pests, controlled environments, structures, and wastes, as well as the plants and animals themselves.

Post-harvest operations considered part of agriculture (such as drying, storage, logistics, production assessment, trimming and separation of plant and animal material) are also covered.

Relevant areas of technology include artificial intelligence, sensors, machine vision, robotics, networking, and simulation modelling.

View Aims & Scope

---

Metrics & Ranking

Impact Factor

Year	Value
2025	8.9

SJR (SCImago Journal Rank)

Year	Value
2024	1.834

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	188

---

---

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

---

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Agricultural and Biological Sciences and Computer Science, designed to support cutting-edge academic discovery.

---

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Deep learning in agriculture: A survey

  Citation: 3143

  Authors: Andreas, Francesc X.

• Deep learning models for plant disease detection and diagnosis

  Citation: 2174

  Authors: Konstantinos P.

• Crop yield prediction using machine learning: A systematic literature review

  Citation: 1177

  Authors: Thomas, Ayalew, Cagatay

• Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

  Citation: 1103

  Authors: Anna, Salah, Brett

• A review of advanced techniques for detecting plant diseases

  Citation: 1023

  Authors: Sindhuja, Ashish, Reza, Cristina

• A comparative study of fine-tuning deep learning models for plant disease identification

  Citation: 893

  Authors: Edna Chebet, Li, Sam, Liu

• Apple detection during different growth stages in orchards using the improved YOLO-V3 model

  Citation: 887

  Authors: Yunong, Guodong, Zhe, Hao, En, Zize

• Precision agriculture—a worldwide overview

  Citation: 861

  Authors: Naiqian, Maohua, Ning

• Verification of color vegetation indices for automated crop imaging applications

  Citation: 793

  Authors: George E., João Camargo

• A review on the practice of big data analysis in agriculture

  Citation: 786

  Authors: Andreas, Andreas, Francesc X.

---