Abstract views: 332 / PDF downloads: 576




Precision Agriculture, IIoT, Spectral analysis, Artificial Intelligence, Remote sensing


According to the current increase rate of the world population it is expected to reach 10 billion people in 2050. In addition, agricultural production area and agricultural labor force is constantly decreasing with the migration of rural population to the city with the use of agricultural areas for residential and industrial purposes. Therefore, it is a necessity to develop and disseminate systematic and efficient production techniques that will provide sufficient nutrition for humanity.

The agricultural sector also benefits greatly from what Industry 4.0 brings. IoT (Internet of Things), AI (Artificial Intelligence), Remote Sensing & ImP (Remote Sensing and Image Processing) techniques have been integrated with GIS (Geographic Information Systems) and have been actively used in agriculture in recent years. In addition to the soil characteristic and meteorological data collected by sensors, high resolution multi-band images taken from satellite systems and unmanned aerial vehicles are transferred to decision support platforms and artificial intelligence support can be used to determine the stress factors of crops and propose instant solution alternatives.

Within the scope of this paper, in a study carried out by HEKTAŞ R & D Center which develops innovation projects in the agricultural sector with the motto of “Pioneer of smart agriculture” general information will be given on the practical use of some of the above mentioned precision agricultural techniques during phenological growth stages of the wheat in Thrace region. 


Download data is not yet available.


ABERSFELDER, S., BOGNER, E., HEYDER, A., FRANKEL, J., 2016, Application and validation of an existing Industry 4.0 guideline for the development of specific recommendations for implementation, Advanced Materials Research, 1140, 465-472.

Akıllı Tarım Platformu, 2019, Türkiye’de Akıllı Tarımın Mevcut Durum Raporu.

ALBERS, A. & B. GLADYSZ & T. PINNER & V. BUTENKO & T. STURMLINGER, 2016, Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems, Procedia CIRP, (52), 262-267.

ALMADA-LOBO, F., 2015, The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES), Journal of Innovation Management, 3(4), 16-21.

BANERJEE, M., CHOO, K. R., LEE, J., 2018, A blockchain future for internet of things security: a position paper, Digital Communications and Networks, 4 (3):149-160.

BARNES, A.P., SOTO, I., EOPRY, V., BARBERO, M.G., 2019, Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers, Land Use Policy 80:163-174.

CHEN, Z., GU, X., HUANG, Y., Yu, T., 2018, Agricultural remote sensing big data: Management and applications, Journal of Integrative Agriculture, (179), 1915–1931.

CLIMATE DIPLOMACY, 2019, Explainer: ‘Desertification’ and the role of climate change [online], Intergovernmental Panel on Climate Change (IPCC),, [Date Accessed: 7 October 2019].

ELOWITZ, M.R., What is Imaging Spectroscopy (Hyperspectral Imaging)?,, [Data Accessed:11 November 2018].

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS, FAOSTAT, 2019, Food and Agriculture Data,, [Data Accessed:19 November 2019].

FORESIGHT, 2011, The Future of Food and Farming, Foresight Programme, Govt Office for Science, London.

GEBBERS, R. and ADAMCHUK, V.I., 2010, Precision Agriculture and Food Security, Science Vol. 327 no. 5967, pp. 828-831.

GENTNER, S., 2016, Industry 4.0: Reality, Future or just Science Fiction? How to Convince Today’s Management to Invest in Tomorrow’s Future! Successful Strategies for Industry 4.0 and Manufacturing IT, Chimia, 70 (9): 628-633.

GRIFFIN, T., BONGIOVANNI, R., LOWEBERG-DEBOER, J., 2010, Worldwide Adoption of Precision Agriculture Technology: The 2010 Update, 10th International Conference of Precision Agriculture, July 18–21, 2010, Denver, Colorado, USA.

GUBÁN, M. and KOVÁCS G., 2017, Industry 4.0 Conception, Acta Technica Corviniensis Bulletin of Engineering, 10(1), 111.

INAN, M., 2004, Orman Varlığının Saptanmasında Uzaktan Algılama Verileri, Doktora, İstanbul Üniversitesi.

KOCA, K.C., 2018, Sanayi 4.0: Türkiye Açısından Fırsatlar ve Tehditler, Sosyoekonomi, Vol. 26(36), 245-252.

LU, Y., 2017, Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration, (6), 1-10.

L3HARRIS GEOSPATIAL, 2013, Vegetation Analysis: Using Vegetation Indices in ENVI,, [Date Accessed: 10 November 2018].

McKINNON, T., 2016, Agricultural Drones; What Farmers Need to Know,, [Data Accessed:13 November 2018].

PIERPAOLA, E., CARLIA, G., PIGNATTIA E. et al., 2013, Drivers of Precision Agriculture

PREUVENEERS, D., & ILIE-ZUDOR, E., 2017, The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0, Journal of Ambient Intelligence and Smart Environments, 9(3):287-298.

PRICE WATERHOUSE COOPERS INTERNATIONAL LIMITED COMPANY, 2016, 2016 Global Industry 4.0 Survey: Industry 4.0: Building the digital enterprise,, [Data Accessed:25 November 2019].

TEKE ve diğ., 2016, Akıllı Tarım Fizibilite Projesi: Hassas Tarım Uygulamaları İçin Havadan Ve Yerden Veri Toplanması, İşlenmesi Ve Analizi, 6. Uzaktan Algılama-Cbs Sempozyumu (Uzal-Cbs 2016), 5-7 Ekim 2016, Adana.

TEMA VAKFI, 2019, Toprağımız için elele,, [Date Accessed: 21 July 2019].

TRAKYA KALKINMA AJANSI Planlama, Programlama ve Koordinasyon Birimi, 2014, Çiftçi Algısı Analizi, Kalitatif Rapor Çiftçi Odak Grup Toplantıları, Edirne,, [Date Accessed: 3 November 2018]

TURKISH STATISTICAL INSTITUTE, 2019, Statistic by Theme, Agriculture,, [Data Accessed:26 October 2019].

UNITED NATIONS, DESA, 2019, Population Division, World Population Prospects 2019,, [Data Accessed:12 November 2019].




How to Cite




Letter to the Editor