Awards & Success Stories

Distinction of Scientific Work “A Compilation of UAV Applications for Precision Agriculture”

The scientific paper entitled “A Compilation of UAV Applications for Precision Agriculture” which was implemented by anagiotis Radoglou-Grammatikis and Panagiotis Sarigiannidis and Thomas Lagkas and Ioannis Moscholios in the context of the MARS project is in the 2th place of research articles with more citations and downloads in the internationally recognized scientific journal ELSEVIER Computer Networks.

Rapid developments in the fields of genetics, robotics and chemistry have led to significant improvements in the field of Precision Agriculture (PA). However, products derived from the primary sector needs to be significantly improved due to the significant increase in the world population.

At the same time, the agricultural sector needs to face significant challenges, such as climate change, the limited availability of arable land, and the growing need for fresh water.

  • According to the Food and Agriculture Organization of the United Nations (FAO) and the International Telecommunication Union (ITU), food production needs to increase significantly (up to 70%) by 2050, in order to serve the needs of the world population.
  • The proper adoption and use of Information and Communication Technologies (ICT) and especially the Internet of Things (IoT), offer important solutions, which can maximize the productivity of agricultural products, such as pesticides and fertilizers, while reducing the operational cost.
  • The advent of IoT and in particular the rapid development of Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNSs can develop efficient and affordable PA applications such as crop gas monitoring and spraying processes.
  • 20 PA applications are analyzed in detail, including implementations such as gas crop monitoring and spraying processes through optimization, machine learning and data analysis methods.

Requirements, challenges and gaps for optimization of GA applications are identified

Distinction of Scientific Work “Towards Smart Farming: Systems, Frameworks and Exploitation of Multiple Sources”

The scientific paper entitled “Towards Smart Farming: Systems, Frameworks and Exploitation of Multiple Sources” which was implemented by Anastasios Lytos and Thomas Lagkas and Panagiotis Sarigiannidis and Michalis Zervakis and George Livanos in the context of the MARS project is in the 8th place of research articles with more citations and downloads in the internationally recognized scientific journal ELSEVIER Computer Networks.

The recent advances in the Information and Communication Technologies (ICT) domain alongside and the need for improvement in agriculture productivity while applying sustainable practices, have paved the way for integrating Big Data into agriculture frameworks. On this context, an analysis for the added value of exploiting multiple sources of data and a classification of existing frameworks can offer a significant value on both research and industry level. Our latest work entitled “Towards smart farming: Systems, frameworks and exploitation of multiple sources” was published in the Computer Networks journal from Elseviers and it is 7th in the most downloaded papers the last 90 days.

With the advent of the Internet of Things (IoT), the generated data increases rapidly, sensors create a significant part of it, and actuators are the result of running software programs, providing a valuable picture of various issues and possible problems. In addition, the digitization of information and higher levels of technical training have created multiple sources of information, such as:

  • Descriptive data are numerical and/or categorical data that have been collected and stored without using any kind of automation, such as analysis of the chemical attributes of the soil in an independent lab.
  • Vector data represent real world features (parcels, pastures, forest, etc.) within the GIS environment and each vector feature have attributes, which consist of text or numerical information that describe them.
  • Satellite and remote sensing data have been collected through satellite-based sensors and they are capable to detect and monitor the physical characteristics of an area by measuring the reflected and emitted radiation from the targeted area.
  • IoT data have been collected through sensors on an IoT network, providing useful info on near real time such as temperature, humidity and more.
  • Drone images are used for the extraction of various Vegetation Indices (Vis) either for identification of cropping patterns
 

The paper entitled "Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning", authored by C. Chaschatzis, C. Karaiskou, E. Mouratidis, E. Karagiannis, and P. Sarigiannidis, was chosen for the cover of the forthcoming issue (Drones, Volume 6, Issue 1 January 2022)

The paper entitled "Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning", authored by C. Chaschatzis, C. Karaiskou, E. Mouratidis, E. Karagiannis, and P. Sarigiannidis, was chosen for the cover of the forthcoming issue (Drones, Volume 6, Issue 1 January 2022). The paper aims to provide solutions towards protecting perennial fruit crops by utilizing machine learning algorithms (Yolov5). The research was co-funded by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, grant number T1EDK-04759.

Drones, Volume 6, Issue 1 (January 2022)..

©2024 MARS Team

en_USEnglish

Log in with your credentials

Forgot your details?

Skip to content