Biosystems Engineering

Aims & Scope

Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain.

The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.

Papers may report the results of experiments, theoretical analyses, design of, or innovations relating to, machines and mechanisation systems, processes or processing methods, equipment and buildings, experimental equipment, laboratory and analytical techniques and instrumentation.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	5.3
2024	4.40

Journal Rank

Year	Value
2024	4568

Journal Citation Indicator

Year	Value
2024	4146

SJR (SCImago Journal Rank)

Year	Value
2024	1.033

Quartile

Year	Value
2024	Q1

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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

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Subjects & Keywords

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

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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.

• Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

  Citation: 1406

  Authors: David J.

• Internet of Things in agriculture, recent advances and future challenges

  Citation: 689

  Authors: Antonis, Nikolaos, Thomas, Constantinos

• Plant disease identification from individual lesions and spots using deep learning

  Citation: 553

  Authors: Jayme Garcia

• Agricultural robots for field operations: Concepts and components

  Citation: 541

  Authors: Avital, Clément

• An Overview of Microwave Processing and Dielectric Properties of Agri-food Materials

  Citation: 541

  Authors: M.S., G.S.V.

• Plant species classification using deep convolutional neural network

  Citation: 527

  Authors: Mads, Henrik, Henrik Skov

• A review on the main challenges in automatic plant disease identification based on visible range images

  Citation: 495

  Authors: Jayme Garcia Arnal

• Factors influencing the use of deep learning for plant disease recognition

  Citation: 436

  Authors: Jayme G.A.

• Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy

  Citation: 433

  Authors: W., A.M., H.

• Precision fish farming: A new framework to improve production in aquaculture

  Citation: 415

  Authors: Martin, Kevin, Tomas, Eirik, Jo Arve, Tim, Harkaitz, Win, Annette, Leif Magne, Christian, Kristoffer R., Morten O., Daniel

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