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Team Members:

a. Praggidi D.
b. Statiri M.
c. Isaakidou Ch.

d. Iatridis Ioan

Team Members:

a. Mpalafouti Af.
b. Kalle Al.
c. Kallariti St.

d. Tsirogianni M.
e. Karra Ioan.

Team Members:

a. Panagiotopoulou Ch.
b. Dikodimitriou Ioan.
c. Filliopoulou Dion.

d. Tsironis Con.
e. Fourfouris Kyr.
f. Dakoutros P.

Team Members:

a. Nikoloulopoulos Al.
b. Ktistis Pan.
c. Fregkou El.

Team Members:

a. Katsaiti M.
b. Antonakakis Al.
c. Fakou El.

In last week I published on LinkedIn number of articles, how to deal with Paris Transport Data in Paris.

A large part of Athens-Patras national road (motor road A8) has been under construction for several years due to delays caused by the financing issues.

We would like to optimise agricultural products' transportation and time-to-marketplaces by monitoring agricultural production per year, per period of year and per area.

For storing and publishing your code, please use GitHub (github.com).

You can use the built-in wiki for storing some info about your project, or the built-in issue system for tickets.

On the https://guides.github.com/ page, you can find beginner's guides, many related questions are answered on https://help.github.com/ or http://stackoverflow.com/questions/tagged/github.

For those who cannot make it to Patras, there is a possibility to join the MedHackathon via a conference tool Adobe Connect. You will be able to join a team and work on a challenging ideas from home.

For remote participation, please go to (only from 13th July onwards): https://connect.cesnet.cz/zz-shongo-hgbczb

Time slot: 13.7.2016 06:00 - 16.7.2016 06:00 (+02:00)
           13.7.2016 04:00 - 16.7.2016 04:00 (UTC)
                             (available 10 minutes before start)
                     Type: One-time room
               Technology: Adobe Connect
                Room name: zz-shongo-hgbczb
                   Record: No
   Number of participants: 60
              ----- How to reach -----
                      URL: https://connect.cesnet.cz/zz-shongo-hgbczb

Introduction

Smart farming (or precision farming) is an environmentally-sound approach that supports farmers in increasing their production and at the same time minimizing costs by controlling the application of inputs (such as water for irrigation, fertilizers, pesticides and other agrochemicals) not only in terms of quantity but also defining different amounts in different parts of a field. Smart farming is based on the collection, management and analysis of data collected from various sources (e.g. from farmers and farm sensors, environmental measurements, remote sensing, satellite data etc.) that are combined with knowledge and allow for informed decision making regarding agricultural practices.

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