Decision Support Systems in Crop Protection

1994 ◽  
Vol 23 (4) ◽  
pp. 281-285 ◽  
Author(s):  
Jonathan D. Knight ◽  
John D. Mumford
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Crop Protection ◽  
Expert Advice ◽  
Management Decision ◽  
Sufficient Information ◽  
Record Keeping ◽  
Pests And Diseases ◽  
The Right

All farmers and growers have at some time faced the decision of whether to control a pest in their crop. In order to make the correct decision the farmer needs access to, and an understanding of, sufficient information relevant to such pest problems. Decision support systems are able to help farmers make these difficult decisions by providing information in an easily understandable and quickly accessed form. The increasing use of computers by farmers for record-keeping and business management is putting the hardware necessary for the implementation of these systems onto more and more farms. The scarcity of expert advice, increasingly complex decisions and reduced economic margins all increase the importance of making the right pest management decision at the right time. It is against this background that decision support systems have an important role to play in the fight against losses caused by pests and diseases.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Management Decision Support Systems.

1980 ◽  
Vol 25 (2) ◽  
pp. 376 ◽  
Author(s):  
Jack William Jones ◽  
Andrew M. McCosh ◽  
Michael S. Scott Morton ◽  
Peter G. Keen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Management Decision Support

Managerial Decision Support in the Post-COVID-19 Era

2021 ◽  
pp. 225-241
Author(s):  
Jan Kalina
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Management Practices ◽  
Support Systems ◽  
Management Decision ◽  
Managerial Decision ◽  
Massive Datasets ◽  
New Development ◽  
Decision Making Processes

The COVID-19 pandemic accelerated trends to digitalization and automation, which allow us to acquire massive datasets useful for managerial decision making. The expected increase of available data (including big data) will represent a potential for an increasing deployment of management decision support systems for more general and more complex tasks. Sophisticated decision support systems have been proposed already in the pre-pandemic times either to assist managers in specific decision-making processes or to perform the decision making fully automatically. Decision support systems are presented in this chapter as perspective artificial intelligence tools contributing to a deep transform of everyday management practices. Attention is paid here to their new development in the quickly transforming post-COVID-19 era and to their role under the post-pandemic conditions. As an original contribution, this chapter presents a vision of information-based management, which far exceed the rather limited pre-pandemic visions of evidence-based management focused primarily on critical thinking.

scholarly journals Benefits and Limitations of Decision Support Systems (DSS) with a Special Emphasis on Weeds

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 548 ◽  
Author(s):  
Panagiotis Kanatas ◽  
Ilias S. Travlos ◽  
Ioannis Gazoulis ◽  
Alexandros Tataridas ◽  
Anastasia Tsekoura ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Weed Management ◽  
Crop Production ◽  
Support Systems ◽  
Management Strategies ◽  
Farming Systems ◽  
Climatic Conditions ◽  
Management Options ◽  
The Right

Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.

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