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Computers and Electronics in Agriculture
40 (2003) 1 /3
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Editorial
Artificial intelligence in agriculture
In agricultural production, there are several different methodologies and processes
which require a rather high energy input. At the same time, the markets require
output products of high quality.
The activities may be classified according to the applied methodology, technology
and application fields (Hashimoto et al., 2002). These issues appear among the
scientific topics of the workshops and conferences organised by the two technical
committees (Modelling and Control in Agricultural Processes and Intelligent
Control in Agricultural Automation) within the International Federation of
Automatic Control (IFAC).
These activities are slowly changing but the most important recent applications
can be grouped as follows:
1) Methodological approaches:
. modelling and simulation methods of dynamical systems and processes in
agriculture,
. control, operation and optimisation methods,
. modelling issues for agro-industry, e.g. uncertainty, randomness, timevariability, strong external disturbances and lack of predictability, and
. artificial intelligence (AI) techniques for modelling, control and operation.
2) Technological approaches:
. photosynthesis of crops under environmental stresses,
. soil /plant atmosphere cycle,
. metabolism of farm animals,
. harvesting systems,
. food processing,
. grading, drying, storage of crops including fruits and vegetables,
. automation, e.g. robotics, vehicle guidance, autonomous vehicles,
. internal transport and packaging,
. distribution and marketing automation, and
. heating, cooling, lighting, and energy saving.
Application fields according to the discussed methodological and technological
approaches:
0168-1699/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S0168-1699(03)00007-3
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. greenhouse technology, e.g. climate control, hydroponics systems, nutrient supply
systems and water management systems,
. environmental and climate control of greenhouses, warehouses and animal
houses,
. post-harvest, e.g. drying systems and control, storage systems control, product
quality protection,
. animal husbandry, e.g. climate control, identification tags, feeder systems, robotic
milking,
. control issues of precision farming, e.g. site-specific operations, positioning,
guidance, weed control, crop protection, management systems, and
. energy issues, alternative energy resources in agriculture.
Concerning the TC activities, likely new technologies and their applications will
come to the focus of interest in the sense of modelling and control. More emphasis is
expected on the quality and safety issues of the end-product in the newly developed
approaches. The man /machine interaction will develop as well.
Concerning the control methodology, approaches in the near future are expected
to deal with such methods as robust control, adaptive control, image processing,
real-time control, uncertainty handling, nonlinear identification methods, especially
the intelligent control. Concerning control systems, it is expected that development
will relate to soft sensors, biosensors and crop monitoring.
Currently, AI methods such as neural networks, genetic algorithms, and fuzzy
control have also a great importance in both modelling and control (Hashimoto,
1997; Murase, 2000).
With the support of the IFAC Life Support System Coordinating Committee, a
workshop series was organised on Artificial Intelligence in Agriculture. The
Workshop aimed to bring together leading researchers and practitioners from
academy and industry in order to promote the emergence or consolidation of bridges
between AI and its applications in agriculture and domains connected to it (in
particular, environmental sciences).
The Workshop topics of interest, in general, include the following main items:
1) Fundamental representation issues:
. management of spatio/temporal knowledge (spatiotemporal reasoning, extension of GIS capabilities, reasoning across several levels of temporal or
spatial granularity),
. management of uncertainly and imprecision (fuzzy logic, belief functions,
Bayesian networks, qualitative reasoning), and
. modelling of multi-agent systems (population modelling, distributed problem
solving).
2) Tools, techniques, algorithms:
. use of emerging technologies (neural networks, genetic algorithms, fuzzy
control, constraint satisfaction, case-based reasoning),
. fusion of multiple sources of information (in particular for interpretation
systems),
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. rise of AI in simulation tools (discrete event systems, use of advanced
representation capabilities, qualitative/quantitative simulation), and
. integration/combination of AI approaches with other approaches.
3) Applications and practical issues:
. successful or novel AI decision support approaches for planning, scheduling,
control, monitoring, prediction or diagnosis problems in agriculture and
related domains,
. commercialised and routinely used AI-based systems, in particular expert and
knowledge-based systems (in laboratories, extension services, farms),
. critical analysis of project failures, and
. validation issues for AI-based decision support systems.
The Fourth International Workshop on ‘‘Artificial Intelligence in Agriculture’’
was held on June 6 /8, 2001, at Budapest, Hungary. It provided a forum for
presentation and discussion of recent advances in the use of artificial intelligence in
different agricultural applications. The presented papers were grouped into four
main sessions, under the headings Fundamentals, decision tools; Neural networks;
Quality and measurements; Fuzzy modelling and control and Modelling and control
in general. Selected papers from the Workshop are published in this issue.
References
Hashimoto, Y. (Ed.), 1997. Special issue: application of artificial neural networks and genetic algorithms
to agricultural systems. Comput. Electron. Agric. 18, 71 /72.
Hashimoto, Y., Farkas, I., Murase, H., Carson, E.R., Sano, A., 2002. Control approaches to bioecological systems: plenary papers, survey papers and milestones. In: Proceedings of the 15th Triennial
World Congress of the International Federation of Automatic Control, Barcelona, Spain, July 21 /26,
2002, pp. 213 /218.
Murase, H. (Ed.), 2000. Special issue: artificial intelligence in agriculture. Comput. Electron. Agric. 29, 1 /
2.
I. Farkas
Department of Physics and Process Control,
University of Agricultural Sciences,
H-2103 Gödöllő,
Hungary
Tel: +36-28-522055; Fax: +36-28-310804
E-mail address: [email protected]