Applications of information technology in agriculture ws ns for environmental monitoring-y. m. awad 2014-408

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This presentation due the workshop at faculty of agriculture - Suss Canal University organized by scientific research group in Egypt (SRGE) on Tuesday 8 April 214

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Applications of information technology in agriculture ws ns for environmental monitoring-y. m. awad 2014-408

  1. 1. ‫التقدم‬‫عن‬ ‫للكشف‬ ‫المعلومات‬ ‫تكنولوجيا‬ ‫في‬‫النباتات‬ ‫أمراض‬ ADVANCES IN INFORMATION TECHNOLOGY FOR DETECTION OF PLANT DISEASES SRGE 08/4/2014 – Cairo Egypt
  2. 2. Scientific Research Group in Egypt www.egyptscience.net
  3. 3. Agenda  Introduction  Plant Diseases  The cost  The solution  Computer-based Detection  Machine Learning Tech.  Expert System  Remote Sensing  WSN  Conclusions
  4. 4. Introduction  UN’s Food and Agriculture Organization (FAO) admitted that food insecurity continues to be a major development problem across the globe*. This problem usually affects to developing countries. *http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/
  5. 5. Introduction Food Security Risk Index 2010 http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/
  6. 6. Plant Diseases  Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products.  The naked eye observation of experts is the main approach adopted in practice for detection and identification of plant diseases
  7. 7. Plant Diseases
  8. 8. The Cost Continuous monitoring of an expert is too expensive and time consuming Expert expenses + Value of damage + Cost of control
  9. 9. The Solution The use of Computer-based techniques to detect the plant diseases in its early stages
  10. 10. The Solution
  11. 11. Computer-based Detection Machine Learning Tech.  Usually, Machine learning techniques are the first choice. The recent researches provide clues on their ability to detect and to identify the plant diseases in its early stages
  12. 12. Machine Learning Tech. Al-Hiary, H., et al. "Fast and Accurate Detection and Classification of Plant Diseases." International Journal of Computer Applications 17
  13. 13. Machine Learning Tech. Al-Hiary, H., et al. "Fast and Accurate Detection and Classification of Plant Diseases." International Journal of Computer Applications 17
  14. 14. Computer-based Detection Machine Learning Tech.  An Indian researcher used ML to establish weather-based prediction models of plant diseases. Kaundal, Rakesh, Amar S. Kapoor, and Gajendra PS Raghava. "Machine learning techniques in disease forecasting: a case study on rice blast prediction." BMC bioinformatics 7.1 (2006): 485.
  15. 15. Computer-based Detection Expert Systems  Expert systems have applications in many domains. They are mostly suited in situations where the expert is not available.  In order to develop an expert system the knowledge has to be extracted from domain expert.
  16. 16. Computer-based Detection Expert Systems  An Indian researcher had developed an Expert System for diagnosis of diseases in Rice Plant Sarma, Shikhar Kr, Kh Robindro Singh, and Abhijeet Singh. "An Expert System for diagnosis of diseases in Rice Plant." International Journal of Artificial Intelligence 1.1 (2010): 26-31.
  17. 17. Computer-based Detection Expert Systems  The rapid development of World Wide Web has provided another way of using expert systems.  A Palestinian Researcher developed Dr. Wheat.
  18. 18. Computer-based Detection Expert Systems
  19. 19. Computer-based Detection Expert Systems  PQ-PickBugs, a multimedia expert system for plant quarantine pest identification
  20. 20. Computer-based Detection Remote Sensing  Hyperspectral sensors onboard of satellites or on AutoCopter to allow to continuously monitor the spatial and temporal physiological and structural changes in a plant production system  Remote sensing provides indications of the growth rate at important development stages. This includes detection of stress due to drought and nutrient deficiency as well as a result of plant diseases or animal pests.
  21. 21. Computer-based Detection Remote Sensing
  22. 22. Computer-based Detection Remote Sensing
  23. 23. Computer-based Detection Wireless Sensor Networks http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/
  24. 24. Conclusion  Information systems and their related applications give a new paradigm in the Agriculture field.  This helps in the early detection of common plant diseases.

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