scholarly journals Bayesian network for decision-support on pest management of tomato fruit borer, H. armigera

2017 ◽  
Vol 6 (4) ◽  
pp. 168 ◽  
Author(s):  
Niranjan Singh ◽  
Neha Gupta
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Pest Management ◽  
Tomato Fruit ◽  
Quantitative Information ◽  
Management Decision ◽  
Management Option ◽  
Tomato Crop ◽  
Ecological Information ◽  
Fruit Borer

Fruit borer (H. armigera) is the key pest of tomato, a prominent vegetable crop grown in northern plains of India. Timely availability of decision-support to the farmers on ‘whether and what management option is required’ is imperative for effective pest management. For decades, the pest economic threshold level (ETL) has been the basis to select an appropriate pest management option. This process requires quantitative information about pest activity, which needs to be scientifically observed in the farmers’ fields. However, a large section of the farming community is not able to scientifically obtain this kind of information. Moreover, in current pest management, decision-making depends upon a large range of agro-ecological information, besides pest activity. In this study, a Bayesian network-based method/model was devised for the selection of an appropriate management option for the effective management of fruit borer in tomato crop, based on tentative agro-ecological information, beside pest activity, that farmers provided. Thus, the resulting method can be used in decision support systems of agriculture with applies information and communication technology to automate and speed up the process of providing pest management decision-support to the farmers.

scholarly journals Evaluation of IPM modules for the management of fruit borer and fruit rot diseases in tomato, Lycopersicon esculentum Miller

2016 ◽  
Vol 8 (1) ◽  
pp. 240-244
Author(s):  
S. D. Sharma ◽  
R. Devlash ◽  
Jitender Kumar ◽  
Brij Bala ◽  
R. S. Jamwal
Keyword(s):  
Lycopersicon Esculentum ◽  
Tomato Fruit ◽  
Tween 80 ◽  
Fruit Rot ◽  
Pheromone Traps ◽  
Tomato Crop ◽  
Net Returns ◽  
Fruit Borer ◽  
Marketable Fruit ◽  
The Cost

Among, five IPM modules tested against tomato fruit borer and fruit rot on tomato, the IPM module (M3) consisting of use of pheromone traps (@ 12 traps/ha) just after transplanting the tomato crop , Lycopersicon esculentum Miller for monitoring the population of Helicoverpa armigera . followed by three foliar sprays commencing with a mixture of lamba-cyhalothrin 5EC @ 0.8ml/L(0.04%) and Dithane Z-78 (Zineb) @ 2.5g/L (0.25%) after 10 days of appearance of moths in the traps (after 30 days of transplanting) followed by spray with a mixture of Helicide (Ha NPV) 100 LE @ 0.5ml/L+ Indofil M-45 @ 2.5g/L (0.25%) + Gur (0.05%) + Tween 80 (0.05%) after 15 days of first spray followed by spray with a mixture of lamba-cyhalothrin 5EC @ 0.8ml/L(0.04%) and moximate (cymoxanil + mancozeb) @ 0.25% after 15 days of the second spray was found to be most effective in minimizing the infestation of fruit borer and fruit rot diseases with 50.00% and 63.45% reduction over control, respectively. This module was also found to be most economic resulting in highest marketable fruit yield (255.94q/ha) and maximum net returns (Rs.10.36) per rupee spent. The present findings are of immense utility as there will be reduction in number of sprays resulting in the cost of production of tomato crop.

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.

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