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Digital Future with Agriculture 4.0

Yıl 2023, Cilt: 17 Sayı: 51, 26 - 30, 25.03.2025

Cansu Kayacan Ali Vardar

Öz

Agriculture is one of the most important fields of activity necessary for human beings to survive. It is predicted that the problem of food demand will increase in the future with climate changes caused by global warming, reduction of natural resources, unconscious use of agricultural land, increasing population. Therefore, it is necessary to emphasize the use of digital technologies for sustainable agriculture. In addition to increasing productivity in agriculture, methods to increase product quality and food safety should be developed. In response to the increasing population in agriculture, migration from villages to cities has led to a decrease in the number of people engaged in agriculture and consequently a decrease in agricultural production. With the development of technology, many methods are being developed for sustainable agriculture such as increasing productivity in the agricultural sector, producing quality seeds, protecting natural resources, and saving labor and production costs. Digital technologies such as precision agriculture, autonomous systems, agricultural robots, artificial intelligence, drone use, and image processing techniques are at the forefront.

Anahtar Kelimeler

Smart agriculture Agricultural robots Agriculture 4.0 Artificial intelligence

Kaynakça

  • Ahmad L, and Nabi F (2021). Smart Intelligent Precision Agriculture. In Agriculture 5.0: Artificial Intelligence, IoT, and Machine Learning (pp. 25-34). CRC Press. Ahmad L, and Nabi F (2021). Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning.
  • Altaş Z, Ozguven MM, Yanar Y (2018). Determination of sugar beet leaf spot disease level (cercospora beticola sacc.) with image processing technique by using drone. Current Investigations In Agriculture And Current Research, 5(3), 621-631, Doi: 10.32474/CIACR.2018.05.000214.
  • Altaş Z, Özgüven MM and Yanar Y (2019). The Use of Image Processing Techniques in Determining Plant Disease and Pest Levels: The Case of Sugar Beet Leaf Spot Disease. International Erciyes Agriculture, Animal and Food Sciences Conference 24-27 April 2019- Erciyes University - Kayseri, Turkey.
  • Araújo SO, Peres RS, Barata J, Lidon F, and Ramalho JC (2021). Characterising the agriculture 4.0 landscape-emerging trends, challenges and opportunities. Agronomy, 11 (4), 667.
  • Astrand B and Baerveldt AJ (2002). An Agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots 13, 21-35, 2002. Kluwer Academic Publishers. Manufactured in The Netherlands.
  • Baluja J, Diago MP, Balda P, Zorer R, Meggio F, Morales F, Tardaguila J (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig. Sci., 511-522.
  • Bozdoğan AM, Bozdoğan NY, Öztekin ME, Keiyinci S (2016). Use of Unmanned Aerial Vehicle in Precision Agriculture. International Multidisciplinary Congress of Eurasia. Odessa, Ukraine. 11-13 July 2016. 686-691.
  • Garcia SN, Osburn BI, Jay-Russell MT (2020). One Health for Food Safety, Food Security, and Sustainable Food Production. Frontiers in Sustainable Food Systems, 4, 1.
  • Grassi M (2014). 5 Actual Uses For Drones In Precision Agriculture Today. http://dronelife.com/2014/12/30/5-actual-uses-drones-precision-agriculture-today/.
  • Kılavuz E, and Erdem İ Social Sciences (NWSAENS), 3C0189, 2019; 14(4),133-157.
  • Liu Y, Ma X, Shu L, Hancke GP, and Abu-Mahfouz AM (2021). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, 17(6). https://doi.org/10.1109/TII.2020.3003910
  • Mathiassen SK, Bak T, Christensen S and Kudsk P (2006). The Effect of Laser Treatment as a Weed Control Method. Biosystems Engineering, 95(4). https://doi.org/10.1016/j.biosystemseng. 2006.08.010
  • Megeto GAS, da Silva AG, Bulgarelli RF, Bublitz CF, Valente AC, Costa DAG (2020). Artificial intelligence applications in the agriculture 4.0. Revista Ciência Agronômica, 51, Special Agriculture 4.0, e20207701.
  • Mekonnen MM and Hoekstra AY (2016). Four billion people facing severe water scarcity {\textbar} {Science} {Advances}. Sci Adv, 2(2).
  • Pakdemirli B, Birişik N, Aslan İ, Sönmez B, Gezici M, Agriculture, T. C., Minister, O., Research And Policies, T, Directorate, G, and Yazar S (2021). The Use of Digital Technologies in Turkish Agriculture and Agriculture 4.0 in the Agriculture-Food Chain. Journal of Toprak Su, 10(1), 78-87. https://doi.org/10.21657/TOPRAKSU.898774
  • Partel V, Kakarla SC, Ampatzidis Y (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture 157, 339-350.
  • Ryan M, Isakhanyan G, Tekinerdogan B (2023). An interdisciplinary approach to artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 95, 2168568.
  • Tamura M, Nimura T, and Naito K (2018). Development of Field Sensor Network System with Infrared Radiation Sensors, In International Conference on Intelligent Interactive Multimedia Systems and Services, Springer, Cham. 74-83 pp.
  • Tan M, Özgüven MM, and Tarhan S (2015). The Use of Drone Systems in Precision Agriculture, 29th Agricultural Mechanization Congress and Energy Congress, 2-5 September Diyarbakır, S:543-547.
  • Tekin A, and Değirmencioğlu A (2010). Agricultural Informatics: Advanced Agricultural Technologies. Academic Informatics'10 - Proceedings of the XIIth Academic Informatics Conference. February 10 - 12, 2010, Muğla.
  • Uzun Y, Bilban M, and Arıkan H (2018). Use of Artificial Intelligence in Agriculture and Rural Development, VI. International KOP Regional Development Symposium, October 26-27, Konya.

Yıl 2023, Cilt: 17 Sayı: 51, 26 - 30, 25.03.2025

Cansu Kayacan Ali Vardar

Öz

Kaynakça

  • Ahmad L, and Nabi F (2021). Smart Intelligent Precision Agriculture. In Agriculture 5.0: Artificial Intelligence, IoT, and Machine Learning (pp. 25-34). CRC Press. Ahmad L, and Nabi F (2021). Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning.
  • Altaş Z, Ozguven MM, Yanar Y (2018). Determination of sugar beet leaf spot disease level (cercospora beticola sacc.) with image processing technique by using drone. Current Investigations In Agriculture And Current Research, 5(3), 621-631, Doi: 10.32474/CIACR.2018.05.000214.
  • Altaş Z, Özgüven MM and Yanar Y (2019). The Use of Image Processing Techniques in Determining Plant Disease and Pest Levels: The Case of Sugar Beet Leaf Spot Disease. International Erciyes Agriculture, Animal and Food Sciences Conference 24-27 April 2019- Erciyes University - Kayseri, Turkey.
  • Araújo SO, Peres RS, Barata J, Lidon F, and Ramalho JC (2021). Characterising the agriculture 4.0 landscape-emerging trends, challenges and opportunities. Agronomy, 11 (4), 667.
  • Astrand B and Baerveldt AJ (2002). An Agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots 13, 21-35, 2002. Kluwer Academic Publishers. Manufactured in The Netherlands.
  • Baluja J, Diago MP, Balda P, Zorer R, Meggio F, Morales F, Tardaguila J (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig. Sci., 511-522.
  • Bozdoğan AM, Bozdoğan NY, Öztekin ME, Keiyinci S (2016). Use of Unmanned Aerial Vehicle in Precision Agriculture. International Multidisciplinary Congress of Eurasia. Odessa, Ukraine. 11-13 July 2016. 686-691.
  • Garcia SN, Osburn BI, Jay-Russell MT (2020). One Health for Food Safety, Food Security, and Sustainable Food Production. Frontiers in Sustainable Food Systems, 4, 1.
  • Grassi M (2014). 5 Actual Uses For Drones In Precision Agriculture Today. http://dronelife.com/2014/12/30/5-actual-uses-drones-precision-agriculture-today/.
  • Kılavuz E, and Erdem İ Social Sciences (NWSAENS), 3C0189, 2019; 14(4),133-157.
  • Liu Y, Ma X, Shu L, Hancke GP, and Abu-Mahfouz AM (2021). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, 17(6). https://doi.org/10.1109/TII.2020.3003910
  • Mathiassen SK, Bak T, Christensen S and Kudsk P (2006). The Effect of Laser Treatment as a Weed Control Method. Biosystems Engineering, 95(4). https://doi.org/10.1016/j.biosystemseng. 2006.08.010
  • Megeto GAS, da Silva AG, Bulgarelli RF, Bublitz CF, Valente AC, Costa DAG (2020). Artificial intelligence applications in the agriculture 4.0. Revista Ciência Agronômica, 51, Special Agriculture 4.0, e20207701.
  • Mekonnen MM and Hoekstra AY (2016). Four billion people facing severe water scarcity {\textbar} {Science} {Advances}. Sci Adv, 2(2).
  • Pakdemirli B, Birişik N, Aslan İ, Sönmez B, Gezici M, Agriculture, T. C., Minister, O., Research And Policies, T, Directorate, G, and Yazar S (2021). The Use of Digital Technologies in Turkish Agriculture and Agriculture 4.0 in the Agriculture-Food Chain. Journal of Toprak Su, 10(1), 78-87. https://doi.org/10.21657/TOPRAKSU.898774
  • Partel V, Kakarla SC, Ampatzidis Y (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture 157, 339-350.
  • Ryan M, Isakhanyan G, Tekinerdogan B (2023). An interdisciplinary approach to artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 95, 2168568.
  • Tamura M, Nimura T, and Naito K (2018). Development of Field Sensor Network System with Infrared Radiation Sensors, In International Conference on Intelligent Interactive Multimedia Systems and Services, Springer, Cham. 74-83 pp.
  • Tan M, Özgüven MM, and Tarhan S (2015). The Use of Drone Systems in Precision Agriculture, 29th Agricultural Mechanization Congress and Energy Congress, 2-5 September Diyarbakır, S:543-547.
  • Tekin A, and Değirmencioğlu A (2010). Agricultural Informatics: Advanced Agricultural Technologies. Academic Informatics'10 - Proceedings of the XIIth Academic Informatics Conference. February 10 - 12, 2010, Muğla.
  • Uzun Y, Bilban M, and Arıkan H (2018). Use of Artificial Intelligence in Agriculture and Rural Development, VI. International KOP Regional Development Symposium, October 26-27, Konya.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Tarımsal Biyoteknoloji (Diğer)
Bölüm Makaleler
Yazarlar

Cansu Kayacan

Ali Vardar

Yayımlanma Tarihi 25 Mart 2025
Gönderilme Tarihi 7 Haziran 2024
Kabul Tarihi 24 Haziran 2024
Yayımlandığı Sayı Yıl 2023 Cilt: 17 Sayı: 51

Kaynak Göster

APA Kayacan, C., & Vardar, A. (2025). Digital Future with Agriculture 4.0. Journal of Biological and Environmental Sciences, 17(51), 26-30.
AMA Kayacan C, Vardar A. Digital Future with Agriculture 4.0. JBES. Mart 2025;17(51):26-30.
Chicago Kayacan, Cansu, ve Ali Vardar. “Digital Future With Agriculture 4.0”. Journal of Biological and Environmental Sciences 17, sy. 51 (Mart 2025): 26-30.
EndNote Kayacan C, Vardar A (01 Mart 2025) Digital Future with Agriculture 4.0. Journal of Biological and Environmental Sciences 17 51 26–30.
IEEE C. Kayacan ve A. Vardar, “Digital Future with Agriculture 4.0”, JBES, c. 17, sy. 51, ss. 26–30, 2025.
ISNAD Kayacan, Cansu - Vardar, Ali. “Digital Future With Agriculture 4.0”. Journal of Biological and Environmental Sciences 17/51 (Mart 2025), 26-30.
JAMA Kayacan C, Vardar A. Digital Future with Agriculture 4.0. JBES. 2025;17:26–30.
MLA Kayacan, Cansu ve Ali Vardar. “Digital Future With Agriculture 4.0”. Journal of Biological and Environmental Sciences, c. 17, sy. 51, 2025, ss. 26-30.
Vancouver Kayacan C, Vardar A. Digital Future with Agriculture 4.0. JBES. 2025;17(51):26-30.

Journal of Biological and Environmental Sciences is the official journal of Bursa Uludag University

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