This document discusses advances in information technology for detecting plant diseases. It outlines how machine learning techniques and expert systems can be used to identify plant diseases early. Remote sensing using hyperspectral sensors and wireless sensor networks are also presented as methods to continuously monitor crops for stress or disease. The conclusion states that sensory-based information and communication technologies can help experts more accurately detect problems affecting crops.

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. 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/

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

5. Introduction
 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

6. Introduction

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

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

9. 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

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

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

12. 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.

13. 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.

14. 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 Jo
Artificial Intelligence 1.1 (2010): 26-31.

15. 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.

16. Computer-based Detection
Expert Systems

17. 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 growers with yield
assessments, shows yield variations across fields and
give information about 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.

18. Computer-based Detection
Remote Sensing

19. Computer-based Detection
Remote Sensing

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

21. Conclusion
 The applications of ICT, especially the sensory
based ones, will help the expert to accurately
detect the problem attached to the corps.