Application of information technology in plant protection in Denmark: from vision to reality

Murali, N. S.; Secher, B. J. M.; Rydahl, Per; Andreasen, Frances M.

doi:10.1016/s0168-1699(99)00011-3

Cited by 21 publications

(13 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In several countries, decision support for weed control is available in the form of recommendations for herbicide selection, herbicide rate and time of application, for example Crop Protection Online (Murali et al ., ; see also https://plantevaernonline.dlbr.dk/cp/documents/InfoFactSheet2.pdf) in Denmark and Gewis (http://www.agrovision.nl/sectoren/teelt/producten_voor_de_teler/crop/gewis/) in the Netherlands. These recommendations are based on data on weed species, crop sensitivity and climatic conditions.…”

Section: Current Applications Of Big Data For Weed Control and Crop Pmentioning

confidence: 99%

Big Data for weed control and crop protection

et al. 2017

View full text Add to dashboard Cite

SummaryFarmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.

show abstract

Section: Current Applications Of Big Data For Weed Control and Crop Pmentioning

confidence: 99%

Big Data for weed control and crop protection

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Perini and Susi (2004) developed a 'systemic' approach to build decision-support systems (DSS) used in agriculture by the advisory service for pest management in Italy. Murali et al (1999) described the impact of a DSS product -'PC -Plant Protection' on agriculture in Denmark after it had been commercialized in 1993. This system is distributed by the Danish Agricultural Advisory Centre and has been well accepted by growers because of increased profits flowing from the reliable and economically valid recommendations provided by the system.…”

Section: Database and Ipmmentioning

confidence: 99%

The role of databases in areawide pest management.

Catana

Elliott²,

Mırık³

et al. 2008

Areawide Pest Management: Theory and Implementation

View full text Add to dashboard Cite

This chapter focuses on the application of DBMS in the field of biology, more specifically in entomology and integrated pest management (IPM). The database concept can contribute to future progress in IPM because it forms an information source for IPM programme development and implementation. One interesting research avenue is the development of forecasting methods to predict pest population dynamics in space and time. The database might contain, for example, the data to assess the relationship between environmental conditions and the occurrence of pest outbreaks. Furthermore, it can provide the data to evaluate the roles of predators and parasitoids as pest population regulators. This is an important point because implementation of a spatially distributed database provides the ability to study the dynamics of insect populations and their interactions using a spatio-temporal approach in a way not possible within the framework of a typical research project.

show abstract

“…com) continues to function as an expert advisory system and covers a range of crops, pests and diseases. PC-Plant Protection, developed in Denmark (Murali et al 1999), also uses expert scoring rules and covers control of weeds, pests and diseases in wheat, with an emphasis on reducing chemical use. EPIPRE in The Netherlands (Zadoks 1981;Rijsdijk 1983) used empirical models to relate observed disease levels to probable losses, but use of the system has now declined as farmers have become educated about the meaning of observations.…”

Section: Systems Modelling For Decisions (A) Backgroundmentioning

confidence: 99%

An engineering approach to modelling, decision support and control for sustainable systems

Day

Audsley

Frost

2007

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability-all essential to sustainable systems.

show abstract

Application of information technology in plant protection in Denmark: from vision to reality

Cited by 21 publications

References 4 publications

Big Data for weed control and crop protection

Big Data for weed control and crop protection

The role of databases in areawide pest management.

An engineering approach to modelling, decision support and control for sustainable systems

Contact Info

Product

Resources

About