Application of Modeling & Information Communication Technology (ICT) in Agriculture Dr. M. Ahsan Latif Department of Computer Science University of Agriculture, Faisalabd

Contents What is Modeling? Why we need it? General Types of Models Modeling in Agriculture What is ICT? ICT in Agriculture GIS GPS Computer Vision Conclusion

Do I have all these things in my computer? What is Modeling? Modeling is way to represent something (real or conceptual) The representation is known as ‘the model’ These representations help me to understand how the things would be in reality

Four Main Reasons / Objectives Why Modeling? Four Main Reasons / Objectives Descriptive To characterize the systems. Prediction To forecast future system behavior Postdiction To explain after-the-fact what caused a given outcome Prescription To get guidance on how a system should be managed to meet some goal

General Types of Models Dynamic Deterministic Static Stochastic Empirical & Mechanistic Mathematical 3D

Modeling in Agriculture Consultative Group on International Agricultural Research (CGIAR) Earth Systems Science Partnership (ESSP) Analysis of Crop Modelling for Climate Change and Food Security Mission Statement The aim of the survey was to collate information, opinions and expert feedback across a wide range of people involved, either directly or in--‐directly, on crop model development and application. The purpose of this was to provide information representing the crop modelling community’s current views on the state of model development, and how they can be improved to support research and decision making on issues of climate change impacts, mitigation and adaptation, and food security. From this it is hoped that improvements in crop modelling capabilities can be utilized to achieve food security, enhancing livelihoods and improving environmental management in the developing world, considering the threats posed by climate change.

Greenhouse Gas Emissions Processes Modelled Water Nutrients Crop Growth CO2 Response Greenhouse Gas Emissions Agriculture

Modeling in Agriculture Open Pdf File ‘Survey Data’

Types of the Models Reported w. r. t Functionality

The Limiting Factors for Model Development

Information Communication Technology

Classical Agriculture Precision Agriculture ICT For Agriculture The state-of-the-art ICT technology has revolutionized the Agriculture Classical Agriculture GIS GPS Computer Vision Precision Agriculture (More Yield + Better Quality) @ Economical Resources (Average Yield + Average Quality) @ Waste of Resources

Geographic Information System A system designed to manipulate and analyze geographical data GIS Geographic data Software Hardware Geographic data: Links the data with spatial information (e. g. topography, hydrography, roads, agriculture, buildings, etc) Depends on Satellite or aerial photography GIS combines Geographic data Data is organized in thematic layers Data is presented in graphical form Helps in decision making

GIS in Agriculture Advantages In a map of agricultural area, for example, one layer each for the following could be produced, i. e., piece of land, soil types, crop yield, specific soil treatment, and irrigation. The interrelationship among these layers can be concluded. Possible applications are yield data analysis, site specific prescription, irrigation planning, terrain analysis, crop growth modeling, etc. Advantages Greater support for precision farming Better understanding of risk factors Higher revenue generation Better resource management Better policy making Possible layers for Agriculture

GPS - Global Positioning System Space & Land based system to determine position precisely Mostly used in Military and Agriculture Millions of users around the world Hand held or vehicle mounted

GPS-Applications in Agriculture

Computer Vision – Applications in Agriculture Automation A Typical Computer Vision System Output (Planting, Fertilizing, Spraying, Harvesting) Sorting The theme of computer vision has been to duplicate the abilities of human vision by electronically perceiving and understanding an image Feature extraction (For Research) Food quality inspection Color, leaf-area, water status, spectral analysis, etc

Conclusion We need to improve and strengthen our mathematics Programming capabilities of the students / faculty should be improved Interdepartmental cooperation for joint research and development needs progress New courses on state-of-the-art technology must be inducted in the curriculum

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