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Strategic Project Management Office (SPMO)
Infrastructure

LoRA Alliance Membership 

Based on the extensive digital telecommunications infrastructure for High Tech Farming created in 2019 – 2020 by SPMO with the utilisation of funds from a variety of sources, AFS has been admitted as an institutional member to the international LoRa Alliance® (https://lora-alliance.org/). The LoRa Alliance® is a nonprofit association that has grown to more than 500 members since its inception in March 2015, becoming the largest and fastest-growing alliance in the technology sector.

The institutional membership of the Alliance includes prestigious organizations such as Carnegie Mellon University, University of Surrey, the China Academy of Information Communications Technology and the Indian Society for Remote Engineering and Data Science. Corporate membership includes technology leaders such as IBM, Cisco, HP, Foxconn, Semtech and Sagemcom as well as the leading product companies such as Schneider, Bosch, Diehl, and Mueller and international brands such as Amazon.

 Μembership to the alliance enhances the kudos of AFS, expands the academic horizons of Perrotis College and strengthens our attractiveness as partners to EU research project consortia.


 

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NUTRIENT MODEL http://smartfarmingafs.gr/  

Agronomists of the Strategic Project Management Office SPMO of the American Farm School developed a nutrient model in order to assist farmers to apply the appropriate type and amounts of fertilization. This model is based on the soil data analysis extracted by lab. It is an extremely useful tool, simple in its application and its purpose is to gather and input information about the physicochemical properties of soils and the available concentration of macro- and micro- nutrients in soil so that the farmer can  take the correct decision about the proper fertilization.

Users input relevant information concerning their cultivation (type of cultivation, cultivated area etc.) and the data for nutrients which was extracted by lab. Subsequently, the model makes the classification of soils and depicts in a type of speedometer the exact stage of the soil nutrient concentration in a range of the optimum or not for each nutrient for specific cultivation. Additionally, it demonstrates if the field needs fertilization or not and gives the optimum advice for fertilization (type and amount).

The extract data is easily readable by all users. All users’ data (input and output) are stored in a cloud-based database and users could access their data anytime. Each farmer can input information and soil analysis data for more than a single field. Furthermore, this tool is connected with google maps in order to depict the field in the map and is connected to established soil sensors and weather stations.

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