SpringerLink
Log in

Decision-Making Tools for Integrated Disease Management

  • Chapter
  • First Online:
Emerging Trends in Plant Pathology

  • 1140 Accesses

Abstract

The decision-making process is the core of any successful integrated disease management programme. The complexity of decision-making process in IDM is much higher as compared to conventional agriculture as it involves multiple factors related to the host, pathogen and environment to be considered. Hence, for taking the most efficient and economic decisions, a farmer or a scientist needs the help of decision-making tools. This need has led to the development of four such decision-making tools viz., warning services, expert systems, decision support systems and onsite devices. They differ in their objective, scope, architecture and complexity of data that they can handle. But the prime objective of these is to help the farming and the scientific community to take the best possible decision regarding plant disease management. At present, their adoption is limited and does not justify the cost and effort required for their development. However, more efficient and user-friendly tools are being developed after rectifying the drawbacks of the previous ones. Their efficient utilization will help in successful plant disease management and lead to the concept of sustainable agriculture.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 213.99
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 267.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 267.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Abu-Naser SS, Kashkash KA, Fayyad M (2008) Develo** an expert system for plant disease diagnosis. J Artif Intell 1:78–85

    Article  Google Scholar 

  • Agrios GN (2005) Plant pathology, 5th edn. Elsevier Academic Press, London, p 4

    Google Scholar 

  • Balasubramani N, Lekshmi PSS (2001) Perceptions of rubber growers on information technology enabledrubber expert system. Indian Res J Ext Edu 8:82–88

    Google Scholar 

  • Beaumont A (1947) The dependence on the weather of the dates of outbreak of potato blight epidemics. Trans Br Mycol Soc 31:45–53

    Article  Google Scholar 

  • Belete T, Boyraz N (2017) Critical review on apple scab (Venturiainaequalis) biology, epidemiology, economic importance, management and defense mechanisms to the causal agent. J Plant Physiol Pathol 5:2–11

    Google Scholar 

  • Beta (2011) Progettocercospora: un concretoaiuto per avviaretrattamenti al momento giusto. Beta News 5:2

    Google Scholar 

  • Bhargava H, Power DJ (2001) Decision support systems and web technologies: a status report. In: America’s Conference on Information Systems, 3–5 August 2001, Boston

    Google Scholar 

  • Bhattacharyya SK, Phadatare SG, Khanna RN, Srivastava DS, Prasad B (1983) Efficacy of some fungicides in controlling late blight of potato in India. Indian J Agric Sci 53:153–157

    Google Scholar 

  • Cabrera VE (2012) DairyMGT: a suite of decision support systems in dairy farm management

    Google Scholar 

  • Carvajal-Yepes M, Cardwell K, Nelson A, Garrett KA, Giovani B, Saunders DGO, Kamoun S, Legg JP, Verdier V, Lessel J, Neher RA, Day R, Pardey P, Gullino ML, Records AR, Bextine B, Leach JE, Staiger S, Tohme J (2019) A global surveillance system for crop diseases. Science 364:1237–1239

    Article  CAS  PubMed  Google Scholar 

  • Chaudhary SD, Pal SC (1959) Forecasting late blight of potatoes in the hills of West Bengal. Am Potato J 36:284–287

    Article  Google Scholar 

  • Chen Y, Hsu CY, Liu L, Yang S (2012) Constructing a nutrition diagnosis expert system. Expert Syst Appl 39:2132–2156

    Article  Google Scholar 

  • De Wolf ED, Isard SA (2007) Disease cycle approach to plant disease prediction. Annu Rev Phytopathol 45:203–220

    Article  PubMed  CAS  Google Scholar 

  • Donatelli M, Magarey RD, Bregaglio S, Willocquet L, Whish JPM, Savary S (2017) Modelling the impacts of pests and diseases on agricultural systems. Agric Syst 155:213–224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dube HC (2013) An introduction to fungi. Scientific Publishers, New Delhi

    Google Scholar 

  • Durkin J (1994) Expert systems: design and development, 1st edn. Prentice Hall, Englewood cliffs. ISBN. 0-02-330970-9

    Google Scholar 

  • Eom S, Kim E (2006) A survey of decision support system applications (1995-2001). J Oper Res Soc 57:1264–1278

    Article  Google Scholar 

  • European Commission (2009) Draft guidance document for establishing IPM principles. Supplement to the Final Report 07.0307/2008/504015/ETU/B3, 49 pp.

  • FAOSTAT (2018) Food and Agriculture Organization of the United Nations, FAOSTAT Statistics Database: www.fao.org/faostat/en/

  • Feigenbaum EA (1992) Personal view of expert systems: looking back and looking ahead, knowledge systems laboratory, Department of Computer Science, Stanford University, Report No. KSL 92-41, 1–20

    Google Scholar 

  • Forrer HR, Gujer HO, Fried PM (1993) PhytoPRE - a comprehensive information and decision support system for late blight in potatoes. In: Workshop on computer based decision support system (DSS) in crop protection. Italy

    Google Scholar 

  • Fry WE, Apple AE, Bruhn JA (1983) Evaluation of potato late blight forecast modified to incorporatehost resistance and fungicide weathering. Phytopathology 73:1054–1059

    Article  Google Scholar 

  • Ganesan V (2006) Decision support system “Crop-9-DSS” for identified crops. In: Proceedings of world academy of science, engineering and technology vol. 12 ISSN 1307- 6884 PWASET Volume

    Google Scholar 

  • Gent DH, Mahaffee WF, McRoberts N, Pfender WF (2013) The use and role of predictive systems in disease management. Annu Rev Phytopathol 51:267–289

    Article  CAS  PubMed  Google Scholar 

  • Gramaje D, Baumgartner K, Halleen F, Mostert I, Sosnowski MR, Urbez-Torres JR, Armengol J (2016) Fungal trunk diseases: a problem beyond grapevines. Plant Pathol 65:355–356

    Article  Google Scholar 

  • Grunwald NJ, Rubio-Covarrubias OA, Fry WE (2000) Potato late blight management in Toluca valley: forecasts and resistant cultivars. Plant Dis 84:410–416

    Article  PubMed  Google Scholar 

  • Gutsche V (1993) PROGEB- a model aided forecasting service for pest management in cereals and potatoes. EPPO Bull 23:577–581

    Article  Google Scholar 

  • Hansen JE, Andersson B, Hermansen A (1995) NEGFRY-A system for scheduling chemical control of late blight in potatoes. In: Dowley LJ et al (eds) Phytophthora infestans 150. Boole Press Ltd., Dublin, pp 201–208

    Google Scholar 

  • Hansen JW (2002) Applying seasonal climate prediction to agricultural production. Agric Syst 74:305–307

    Article  Google Scholar 

  • Harrison JG (1992) Effects of the aerial environment on late blight of potato foliage – a review. Plant Pathol 41:384–416

    Article  Google Scholar 

  • Jones AL, Lillevik SL, Fisher PD, Stebbins TC (1980) A microcomputer-based instrument to predict primary apple scab infection periods. Plant Dis 64:69–72

    Article  Google Scholar 

  • Jorgensen LN, Noe E, Langrad AM, Jensen JE, Orum JE, Rydahl P (2006) Decision support systems: barriers and farmer’s need for support. Paper presented at the EPPO conference on ‘computer aids for plant protection’ in Wageningen, 17–19 October, 2006

    Google Scholar 

  • Kleinhenz B, Kristina F, Joachim K, Dietma R (2007) SIMBLIGHT1 – a new model to predict first occurrence of potato late blight. EPPO Bull 37:339–343

    Article  Google Scholar 

  • Krause RA, Massie LB, Hyre RA (1975) BLITECAST: a computerized forecast of potato late blight. Plant Dis Rep 59:95–98

    Google Scholar 

  • Kumbhar V, Singh PT (2013) A comprehensive study of application of decision support system in agriculture in Indian context. Int J Comp Appl 63:6–11

    Google Scholar 

  • Mac Hardy WE (1979) A simplified, non computerized program forecasting potato late blight (Phytophthora infestans). Plant Dis Rep 63:21–25

    Google Scholar 

  • MacHardy WE, Gadoury DM (1989) A revision of mills criteria for predicting apple scab infection periods. Phytopathology 79:304–310

    Article  Google Scholar 

  • MacKenzie DR (1981) Scheduling fungicide application for potato late blight with BLITECAST. Plant Dis 65:394–399

    Article  Google Scholar 

  • Madden L, Pennypacker SP, MacNab AA (1978) FAST, a forecast system for Alternariasolanion tomato. Phytopathology 68:1354–1358

    Article  Google Scholar 

  • Mahaman BD, Passam HC, Sideridis ABYialouris CP. (2003) DIARES-IPM: a diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems. Agric Syst 76:1119–1135

    Article  Google Scholar 

  • Manos B, Ciani A, Bournaris T, Vassiliadou I, Papathanasiou J (2004) A taxonomic survey of decision support systems in agriculture. Agric Econ Rev 5:80–94

    Google Scholar 

  • Marakas GM (2003) Decision support systems in the 21st century. Prentice Hall, Upper Saddle River. ISBN: 9780130922069

    Google Scholar 

  • March JG (1994) Primer on decision making: how decisions happen, vol 289. The Free Press, New York

    Google Scholar 

  • McCown RL (2002) Changing systems for supporting farmers’ decisions: problems, paradigms, and prospects. Agric Syst 74:179–220

    Article  Google Scholar 

  • Mir SA, Quadri SMK (2009) Climate change, intercrop**, Pest control and beneficial microorganisms. Climate change, intercrop**, Pest control and beneficial microorganisms (January 1970). https://doi.org/10.1007/978-90-481-2716-0

  • Mosseddaq F, Dnidane S, Lahlou M (2005) Integrated wheat N nutrition management in morocco: a decision support model paper presented at EFITA/WCCA, 2005, 25–28 July 2005, Vila Real, Portugal

    Google Scholar 

  • Odile C, David-Mathieu T, Tristan J, Anne Sophie W (2010) Disease decision support systems: their impact on disease management and durability of fungicide effectiveness. Fungicides, (May 2014). https://doi.org/10.5772/13335

  • Olivier JM, Lambert C, Lefeuvre M (1983) Application du thermohumectographe KIT- INRA etude des risqué de tavelure du pommier a l’echelle du Maine-et-Loire (France). Bull OEPP 13:47–56

    Article  Google Scholar 

  • Parker M, Warmund M (2011) Effect of temperature on apple trees – eXtension. Extension. http://articles.extension.org/pages/60619/effect-of-temperature-on-apple-trees

    Google Scholar 

  • Pham DT, Pham PTN (1988) Expert systems in mechanical and manufacturing engineering. Int J Adv Manuf Tech 3:3–21

    Article  Google Scholar 

  • Rabbinge R, Rossing WAH, Van Der Werf W (1993) Systems approaches in epidemiology and plant disease management. Neth J Plant Pathol 99:161–171

    Article  Google Scholar 

  • Reddy MN, Rao NH (1995) GIS Based decision support systems in agriculture. National Academy of Agricultural Research Management, Rajendranagar, pp 1–11

    Google Scholar 

  • Rossi V, Ponti I, Cravedi P (2000) The status of warning services for plant pests in Italy. EPPO Bull 30:19–29

    Article  Google Scholar 

  • Rossi V, Caffi T, Salinari F (2012) Hel** farmers face the increasing complexity of decision-making for crop protection. Phytopathol Mediterr 51:457–479

    Google Scholar 

  • Rotem J, Cohen Y, Putler J (1971) Relativity of limiting and optimum inoculum loads, wetting duration and temperature for infection by Phytophthora infestans. Phytopathology 61:275–278

    Article  Google Scholar 

  • Runno E, Koppel M (2002) Validation of potato late blight control system NEGFRY in estonian conditions. Latvia University of Agriculture, Latvia, pp 61–64

    Google Scholar 

  • Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3:430–439

    Article  PubMed  Google Scholar 

  • Schwabe WFS (1979) Changes in scab susceptibility of apple leaves as influenced by age. Phytophylactica 11:53–56

    Google Scholar 

  • Shafinah K, Sahari N, Sulaiman R, Yusoff MM, Ikram MM (2013) A framework of an expert system for crop pest and disease management. J Theor Appl Inf Technol 58:182–190

    Google Scholar 

  • Shtienberg D (2013) Will decision-support systems be widely used for the management of plant diseases. Annu Rev Phytopathol 51:1–16

    Article  CAS  PubMed  Google Scholar 

  • Singh KP, Kumar J (2005) IPM of apple diseases. Techanical Bulletin, GBPUAT, CFHA 6:1–48

    Google Scholar 

  • Singh RS (2009) Plant disease, 9th edn. Oxford and IBH Publishing Co. Pvt. Ltd, New Delhi, pp 271–286

    Google Scholar 

  • Singh KP, Kumar J (1999) Studies on ascospore maturity of venturia inaequalis, the apple scab pathogen, in central himalayas of India. J Mycol Plant Pathol 29:408–415

    Google Scholar 

  • Singh KP, Kumar J (2005) IPM of apple diseases. Tech Bull GBPUAT CFHA 6:1–48

    Google Scholar 

  • Singh KP, Kumar J (2008) Disease warning system for scab of apple: a field study. GBPUAT CFHA 22:1–18

    Google Scholar 

  • Singh KP, Kumar J (2009) Potential ascospore dose of apple scab fungus, Venturiainaequalis, from Indian Himalayas. Indian J Agric Sci 79:184–189

    Google Scholar 

  • Singh BP, Islam A, Sharma VC, Shekhawat GS (2000) JHULSACAST: a computerized forecast of potato late blight in Western Uttar Pradesh. J Indian Potato Assoc 27:25–34

    Google Scholar 

  • Singh KP, Kumar J, Singh HB (2001) Curative and protective action of ergosterol-biosynthesis inhibiting fungicides in relation to infection periods against apple scab in Uttaranchal Himalayas. Indian J Plant Path 19:34–38

    Google Scholar 

  • Singh KP, Kumar J, Kumar B (2010) GBPUAT and apple disease research in the Gangotri valley region of India. In: Singh KP, Shahi DK (eds) Microbial diversity and plant disease management. VDM Verlag, Saarbrücken, pp 276–301

    Google Scholar 

  • Singh VK, Shailbala, Pundhir VS (2012) Forecasting models: an effective toos for potato late blight management. In: Singh VK, Singh Y, Singh A (eds) Eco-friendly Innovative Approaches in Plant Disease Management. International Book Distributors and Publisher, New Delhi, pp 102–112

    Google Scholar 

  • Singh KP, Singh A, Singh UP (2015) Phenolic acid content of some apple cultivars with varying degrees of resistance to apple scab. Int J Fruit Sci 15:267–280

    Article  Google Scholar 

  • Singh BP, Govindakrishnan PM, Islam A, Rawat S, Sharma S, Sreekumar J (2016a) INDO-BLIGHTCAST – a model for forecasting late blight across agroecologies. Int J Pest Manag. https://doi.org/10.1080/09670874.2016.1210839

  • Singh KP, Kumar J, Singh A, Prasad RK, Singh RP, Prasad D (2016b) Maturation, ascospores discharge pattern and relevance of mills criteria for predicting apple scab infection period in India. Plant Path J 15:108–123

    Article  CAS  Google Scholar 

  • Singh KP (2019) Aerobiology, epidemiology and management strategies in apple scab: science and its applications. Indian Phytopathol 72:381–408

    Article  Google Scholar 

  • Sodtke RM (2005) A Multi-objective DSS for cover crop management processing fuzzy expert knowledge. Paper presented at EFITA/WCCA, 2005, 25–28 July 2005, Vila Real, Portugal

    Google Scholar 

  • Stensvand A, Gadoury DM, Amundsen T, Semb L, Seem RC (1997) Ascospore release and infection of apple leaves by conidia and ascospores of Venturiaina equalis at low temperature. Phytopathology 87:1046–1053

    Article  CAS  PubMed  Google Scholar 

  • Sys S, Soenen A (1970) Investigations on the infection criteria of scab on apple with respect to the table of Mills and La Plante. Agriculture, Heverlee, Belgium 18:3–8

    Google Scholar 

  • Thakur VS, Khosla K (1999) Relevance of Mills infection periods to apple scab (Venturiaina equalis) prediction and rescheduling fungicide applications in Himanchal Pradesh. Indian J Agric Sci 69:152–156

    Google Scholar 

  • Turban E (1995) Decision support and expert systems: management support systems. In: Prentice Hall. NJ, Englewood cliffs

    Google Scholar 

  • Van Everdingen E (1926) Het.Verband tusschen de weergesteldhied en de aarolppelziekte, Phytopthorainfestans (the relation between weather conditions and potato blight, Phytophthora infestans) Tijdschr. Plan Theory 32:129–140

    Google Scholar 

  • Wallin JR (1962) Summary of recent progress in predicting late blight epidemics in United States and Canada. Am Potato J. 39:306–312

    Article  Google Scholar 

  • Yialouris C, Passam HC, Sideridis A, Metin C (1997) VEGES—A multilingual expert system for the diagnosis of pests, diseases and nutritional disorders of six greenhouse vegetables. Comput Electron Agric 19:55–67

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Plant Pathology, College of Agriculture, Pantnagar, Uttarakhand, India

    K. P. Singh, T. Aravind & C. S. Karibasappa

  2. Institute of Crop Science and Resource Conservation, Crop Science Group, Katzenburgweg 5, University of Bonn, Bonn, Germany

    Amit Kumar Srivastava

Authors
  1. K. P. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Aravind
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Kumar Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. S. Karibasappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Plant Pathology, G.B. Pant University of Agriculture & Technology, Pantnagar, India

    Krishna P. Singh

  2. Department of Plant Pathology, University of Agricultural Sciences Dharwad, Dharwad, Karnataka, India

    Shamarao Jahagirdar

  3. Department of Mycology and Plant Pathology, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Birinchi Kumar Sarma

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, K.P., Aravind, T., Srivastava, A.K., Karibasappa, C.S. (2021). Decision-Making Tools for Integrated Disease Management. In: Singh, K.P., Jahagirdar, S., Sarma, B.K. (eds) Emerging Trends in Plant Pathology . Springer, Singapore. https://doi.org/10.1007/978-981-15-6275-4_31

Download citation

Publish with us

Policies and ethics

Search

Navigation