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Information Processing in Agriculture

The Journal of the China Agricultural University

Information Processing in Agriculture (IPA) was established in 2013 to encourage the development of science and technology related to information processing in agriculture, through the following aims:

• Promote the use of knowledge and methods from information processing technologies in agriculture;
• Report on experiences and publications of institutes, universities, and government, as well as profitable technologies for agriculture;
• Provide a platform and opportunities for exchanging knowledge, strategies, and experiences among information processing researchers worldwide;
• Promote and encourage interactions among agriculture scientists, meteorologists, biologists (pathologists/entomologists), information technology professionals, and other stakeholders to develop and implement methods, techniques, and tools related to information processing technology in agriculture;
• Create and promote expert groups for the development of agro-meteorological databases, crop and livestock modeling, and applications for the development of crop performance-based decision support systems.

Topics of interest include, but are not limited to, the following aspects:
• Smart sensors, biosensors and bioelectronics, material and molecular innovations for chemical and biological sensing, sensors, and automation and control systems for agriculture
• Wireless sensor networks, 4G, NB-IOT, and 5G applications in agriculture
• Remote sensing and discrete element modeling (DEM) applications in agriculture
• Simulation, optimization, modeling, and automated control
• Decision support systems, intelligent systems, and artificial intelligence
• Machine vision, computer vision, image processing and automation, and imaging technologies for high-throughput phenotyping
• Advances in spectroscopy and hyperspectral properties of biological products
• Advanced computational approaches for solving agricultural and biological engineering problems
• Computational fluid dynamics (CFD) applications in agriculture
• Inspection and traceability for food quality
• Precision agriculture, intelligent instruments, robotics, and co-robotics for agriculture
• Internet of things, cloud computing, and precision farming
• Big data, data mining, and data analysis for agricultural applications
• Unmanned aerial vehicles (UAVs) for sensing, imaging, and agricultural aquacultural applications

View full aims and scope


Editor in Chief: Professor Daoliang Li
View full editorial board


Journal Metrics
CiteScore: 6.2
CiteScore:
: 6.2
CiteScore measures the average citations received per peer-reviewed document published in this title. CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 – 19).
Source Normalized Impact per Paper (SNIP): 2.338
Source Normalized Impact per Paper (SNIP):
2019: 2.338
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 0.756
SCImago Journal Rank (SJR):
2019: 0.756
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Imprint: KeAi
ISSN: 2214-3173
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