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

1. ‫التقدم‬‫عن‬ ‫للكشف‬ ‫المعلومات‬ ‫تكنولوجيا‬ ‫في‬‫النباتات‬ ‫أمراض‬
ADVANCES IN INFORMATION TECHNOLOGY FOR
DETECTION OF PLANT DISEASES
SRGE 08/4/2014 – Cairo Egypt

2. Scientific Research Group in Egypt
www.egyptscience.net

3. Agenda
 Introduction
 Plant Diseases
 The cost
 The solution
 Computer-based Detection
 Machine Learning Tech.
 Expert System
 Remote Sensing
 WSN
 Conclusions

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. Introduction
Food Security Risk Index 2010
http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/

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. Plant Diseases

8. The Cost
Continuous monitoring of an
expert is too expensive and time
consuming
Expert expenses +
Value of damage +
Cost of control

9. The Solution
The use of Computer-based techniques to
detect the plant diseases in
its early stages

10. The Solution

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. Machine Learning Tech.
Al-Hiary, H., et al. "Fast and Accurate Detection and Classification of Plant Diseases." International Journal of Computer
Applications 17

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. 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. 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. 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. 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. Computer-based Detection
Expert Systems

19. Computer-based Detection
Expert Systems
 PQ-PickBugs, a multimedia expert system for plant
quarantine pest identification

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. Computer-based Detection
Remote Sensing

22. Computer-based Detection
Remote Sensing

23. Computer-based Detection
Wireless Sensor Networks
http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/

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.