أمراض النباتات في تكنولوجيا المعلومات للكشف عن التقدم

ADVANCES IN INFORMATION TECHNOLOGY FOR

DETECTION OF PLANT DISEASES

SRGE 08/4/2014 – Cairo Egypt

Scientific Research Group in Egypt www.egyptscience.net

Agenda

Introduction

Plant Diseases

The cost

The solution

Computer-based Detection

Machine Learning Tech.

Expert System

Remote Sensing

WSN

Conclusions

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/

Introduction

Food Security Risk Index 2010

http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/

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

Plant Diseases

The Cost

Continuous monitoring of an

expert is too expensive and time

consuming

Expert expenses +

Value of damage +

Cost of control

The Solution

The use of Computer-based techniques to

detect the plant diseases in

its early stages

The Solution

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

Machine Learning Tech.

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

Applications 17

Machine Learning Tech.

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

Applications 17

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.

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.

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.

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.

Computer-based Detection

Expert Systems

Computer-based Detection

Expert Systems

PQ-PickBugs, a multimedia expert system for plant

quarantine pest identification

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.

Computer-based Detection

Remote Sensing

http://plantstress.bioiberica.com/Training/What_is/Plant_stress.html

Computer-based Detection

Remote Sensing

Computer-based Detection

Wireless Sensor Networks

http://www.libelium.com/es/food_sustainability_monitoring_sensor_network/

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.