A DIGITAL AGRICULTURE APPLICATION ACCORDING TO THE SCALE OF AGRICULTURE 4.0: FARM MONITORING AND MANAGEMENT SYSTEM
Abstract
Agriculture is one of the fundamental elements of economic development worldwide. With the emergence of the agriculture 4.0 concept in recent years, a transformation has begun in the agricultural sector. Agriculture 4.0 entails the integration of digital technologies such as sensors, the Internet of Things (IoT), artificial intelligence, and robotics into agricultural processes to monitor and optimize production processes. As the need for Agriculture 4.0 technologies becomes increasingly evident, investments in this field are rapidly increasing both globally and in our country. The effects of digital transformation are also evident in livestock farming, which is an important area of the agricultural sector. Within livestock farming, Agriculture 4.0 technologies offer advantages such as increasing farm efficiency, maintaining animal health and welfare, optimizing production processes, and efficient resource utilization. Herd management systems hold a significant place within Agriculture 4.0. These systems are software applications that enable farmers to monitor and manage their animals more efficiently. This study discusses the technical and functional features, modules, and operation of the Farm Monitoring and Management System, a herd management software developed as part of the agriculture 4.0 scale and an expandable ecosystem. Furthermore, details of the integrated laboratory analysis application and the mobile application are also provided. The project represents an ecosystem that addresses the critical needs of the region and the country in livestock farming, namely healthy data recording, farm monitoring, and management, enabling the creation of future projections and increased efficiency through conducted analyses.
Supporting Institution
Burdur Mehmet Akif Ersoy University Scientific Research Projects Commission
Project Number
2017K12-41003-04
Thanks
This study was supported by Burdur Mehmet Akif Ersoy University Scientific Research Projects Commission with project number 2017K12-41003-04.
References
- Alcantara, I.R., Schimidt, J.G.A., Vian, C.E.F., Belardo, G, 2021. Agriculture 4.0: Origin and Features in the World and Brazil. Quaestum, 2, 1-14.
- Ateş, M., Şahin, Y., 2021. Evaluation of Industry 4.0 Applications for Agriculture Using AHP Methodology. Kocaeli Journal of Science and Engineering, 4(1), 39-45.
- Beck, M.A., Liu, C.Y., Bidinosti, C.P., Henry, C.J., Godee, C.M., Ajmani, M., 2020. An Embedded System for the Automated Generation of Labeled Plant Images to Enable Machine Learning Applications in Agriculture. Plos One, 15, (12), e0243923.
- Bernhardt, H., Bozkurt, M., Brunsch, R., Colangelo, E., Herrmann, A., Horstmann, J., Kraft, M., Marquering, J., Steckel, T., Tapken, H., Weltzien, C., Westerkamp, C., 2021. Challenges For Agriculture Through Industry 4.0. Agronomy, 11, (10), 1935.
- Bi, X., Wen, B., Zou., W., 2022. The Role of Internet Development in China’s Grain Production: Specific Path and Dialectical Perspective. Agriculture, 12, (3), 377.
- Ciani, M., Lippolis, A., Fava, F., Rodolfi, L., Niccolai, A., Tredici., M.R., 2021. Microbes: Food For the Future. Foods, 10, (5), 971.
- Cockburn, M., 2020. Application and Prospective Discussion of Machine Learning for The Management of Dairy Farms. Animals, 10, (9), 1690.
- Dayıoğlu, M.A., Turker, U., 2021. Digital Transformation for Sustainable Future-Agriculture 4.0: A review. Journal of Agricultural Sciences, 27, (4), 373-399.
- Fukatsu, T., Nanseki, T., 2009. Monitoring System for Farming Operations with Wearable Devices Utilized Sensor Networks. Sensors, 9, (8), 6171-6184.
- Garcia, S.N., Osburn, B.I., Jay-Russell, M.T., 2020. One Health for Food Safety, Food Security, and Sustainable Food Production. Frontiers in Sustainable Food Systems, 4, 1.
- Khanal, A.R., Gillespie, J., MacDonald, J., 2010. Adoption of Technology, Management Practices, and Production Systems in US Milk Production. Journal of Dairy Science, 93, (12), 6012-6022.
- Liu, B., Tao, W., Feng, D., Wang, Y., Heizatuola, N., Ahemetbai, T., Wu, W., 2022. Revealing Genetic Diversity and Population Structure of Endangered Altay White-headed Cattle Population Using 100 K SNP Markers. Animals, 12, (22), 3214.
- Morrone, S., Dimauro, C., Gambella, F., Cappai, M.G., 2022. Industry 4.0 and Precision Livestock Farming (PLF): An Up To Date Overview Across Animal Productions. Sensors, 22, (12), 4319.
- Neethirajan, S., Kemp, B., 2021. Digital Livestock Farming. Sensing and Bio-Sensing Research, 32, 100408.
- Oduniyi, O.S., Antwi, M.A., Mukwevho, A.N, 2022. Assessing Emerging Beef Farmers Participation in High-Value Market and its Impact on Cattle Sales in South Africa. Int. J. Agril. Res. Innov. Tech., 11, (2), 27-36.
- OECD., 2018. How Digital Technologies are Impacting the Way We Grow and Distribute Food. https://one.oecd.org
- Ramos, P.H.B, Pedroso, M.C., 2021. Classification and Categorization of Brazilian Agricultural Startups (Agtechs). INMR, 18, (3), 237-257.
- Sharma, K., Sharma, C., Sharma, S., Asenso, E., 2022. Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling. Journal of Sensors, 2022, 1-19.
- Suwanan, A.F., Rori, A.M., Kurniawan, D.T., 2021. The Critical Review of Agriculture Technological Transfer in the Era of Decentralization. E3S Web Conf., 306, 03021.
- Tarım ve Orman Bakanlığı, 2020. AB Çerçeve Program Projeleri. Erişim Tarihi: 21.07.2023 https://www.tarimorman.gov.tr/TAGEM/Menu/9/Ab-Cerceve-Program-Projeleri
Abstract
Tarım, dünya genelinde ekonomik gelişmenin temel unsurlarından biridir. Son yıllarda ortaya çıkan Tarım 4.0 kavramı ile birlikte tarım sektöründe bir dönüşüm meydana gelmeye başlamıştır. Tarım 4.0 üretim süreçlerini izlemek, optimize etmek için dijital teknolojiler, sensörler, nesnelerin interneti, yapay zekâ, robotik gibi teknolojilerin tarımsal süreçlere entegre edilmesini ifade eder. Tarım 4.0 teknolojilerine duyulan ihtiyaç giderek daha belirgin hale gelirken dünyada ve ülkemizde bu alandaki yatırımlar da hızla artış göstermektedir. Tarım sektörünün önemli sahalarından biri olan hayvancılıkta da dijital dönüşümün etkileri görülmektedir. Hayvancılık alanında Tarım 4.0 teknolojileri çiftliklerin verimliliğini artırarak, hayvan sağlığı ve refahını kontrol altında tutma, üretim süreçlerini optimize etme ve kaynakları etkin kullanma gibi avantajlar sağlamaktadır. Tarım 4.0 içerisinde sürü yönetim sistemlerinin önemli bir yeri vardır. Sürü yönetim sistemleri, çiftçilerin hayvanlarını daha verimli bir şekilde izlemelerini ve yönetmelerini sağlayan yazılımlardır. Bu çalışmada Tarım 4.0 ölçeğinde bir sürü yönetim yazılımı ve genişletilebilir ekosistem olarak geliştirilen Çiftlik İzleme ve Yönetim Sistemi (ÇİYS) projesinin teknik ve fonksiyonel özeliklerinden, modülleri ve işleyişinden söz edilmektedir. Ayrıca ana projeye entegre olan laboratuvar analiz uygulaması (ÇİYSLAB) ve mobil uygulamanın detaylarına da yer verilmiştir. Proje, hayvancılık alanında bölgenin ve ülkenin en büyük ihtiyaçlarından biri olan sağlıklı veri kaydı, çiftlik izleme ve yönetimini sağlayan bir ekosistem olup, gerçekleştirilen analizlerle geleceğe dönük projeksiyonlar oluşturulmasına ve verim artışına olanak sağlamaktadır.
Supporting Institution
Burdur Mehmet Akif Ersoy Üniversitesi Bilimsel Araştırma Projeleri Komisyonu
Project Number
2017K12-41003-04
Thanks
Bu çalışma, Burdur Mehmet Akif Ersoy Üniversitesi Bilimsel Araştırma Projeleri Komisyonunca, 2017K12-41003-04 proje numarası ile desteklenmiştir.
References
- Alcantara, I.R., Schimidt, J.G.A., Vian, C.E.F., Belardo, G, 2021. Agriculture 4.0: Origin and Features in the World and Brazil. Quaestum, 2, 1-14.
- Ateş, M., Şahin, Y., 2021. Evaluation of Industry 4.0 Applications for Agriculture Using AHP Methodology. Kocaeli Journal of Science and Engineering, 4(1), 39-45.
- Beck, M.A., Liu, C.Y., Bidinosti, C.P., Henry, C.J., Godee, C.M., Ajmani, M., 2020. An Embedded System for the Automated Generation of Labeled Plant Images to Enable Machine Learning Applications in Agriculture. Plos One, 15, (12), e0243923.
- Bernhardt, H., Bozkurt, M., Brunsch, R., Colangelo, E., Herrmann, A., Horstmann, J., Kraft, M., Marquering, J., Steckel, T., Tapken, H., Weltzien, C., Westerkamp, C., 2021. Challenges For Agriculture Through Industry 4.0. Agronomy, 11, (10), 1935.
- Bi, X., Wen, B., Zou., W., 2022. The Role of Internet Development in China’s Grain Production: Specific Path and Dialectical Perspective. Agriculture, 12, (3), 377.
- Ciani, M., Lippolis, A., Fava, F., Rodolfi, L., Niccolai, A., Tredici., M.R., 2021. Microbes: Food For the Future. Foods, 10, (5), 971.
- Cockburn, M., 2020. Application and Prospective Discussion of Machine Learning for The Management of Dairy Farms. Animals, 10, (9), 1690.
- Dayıoğlu, M.A., Turker, U., 2021. Digital Transformation for Sustainable Future-Agriculture 4.0: A review. Journal of Agricultural Sciences, 27, (4), 373-399.
- Fukatsu, T., Nanseki, T., 2009. Monitoring System for Farming Operations with Wearable Devices Utilized Sensor Networks. Sensors, 9, (8), 6171-6184.
- Garcia, S.N., Osburn, B.I., Jay-Russell, M.T., 2020. One Health for Food Safety, Food Security, and Sustainable Food Production. Frontiers in Sustainable Food Systems, 4, 1.
- Khanal, A.R., Gillespie, J., MacDonald, J., 2010. Adoption of Technology, Management Practices, and Production Systems in US Milk Production. Journal of Dairy Science, 93, (12), 6012-6022.
- Liu, B., Tao, W., Feng, D., Wang, Y., Heizatuola, N., Ahemetbai, T., Wu, W., 2022. Revealing Genetic Diversity and Population Structure of Endangered Altay White-headed Cattle Population Using 100 K SNP Markers. Animals, 12, (22), 3214.
- Morrone, S., Dimauro, C., Gambella, F., Cappai, M.G., 2022. Industry 4.0 and Precision Livestock Farming (PLF): An Up To Date Overview Across Animal Productions. Sensors, 22, (12), 4319.
- Neethirajan, S., Kemp, B., 2021. Digital Livestock Farming. Sensing and Bio-Sensing Research, 32, 100408.
- Oduniyi, O.S., Antwi, M.A., Mukwevho, A.N, 2022. Assessing Emerging Beef Farmers Participation in High-Value Market and its Impact on Cattle Sales in South Africa. Int. J. Agril. Res. Innov. Tech., 11, (2), 27-36.
- OECD., 2018. How Digital Technologies are Impacting the Way We Grow and Distribute Food. https://one.oecd.org
- Ramos, P.H.B, Pedroso, M.C., 2021. Classification and Categorization of Brazilian Agricultural Startups (Agtechs). INMR, 18, (3), 237-257.
- Sharma, K., Sharma, C., Sharma, S., Asenso, E., 2022. Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling. Journal of Sensors, 2022, 1-19.
- Suwanan, A.F., Rori, A.M., Kurniawan, D.T., 2021. The Critical Review of Agriculture Technological Transfer in the Era of Decentralization. E3S Web Conf., 306, 03021.
- Tarım ve Orman Bakanlığı, 2020. AB Çerçeve Program Projeleri. Erişim Tarihi: 21.07.2023 https://www.tarimorman.gov.tr/TAGEM/Menu/9/Ab-Cerceve-Program-Projeleri
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Project Number | 2017K12-41003-04 |
| Early Pub Date | December 23, 2023 |
| Publication Date | December 29, 2023 |
| Published in Issue | Year 2023 Volume: 7 Issue: 2 |
