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Analysis of Crop Monitoring Technologies Used in Crop Agriculture

Yıl 2025, Cilt: 15 Sayı: 1, 132 - 145, 26.06.2025

Bora Aslan , Füsun Yavuzer Aslan

https://doi.org/10.53518/mjavl.1599535

Öz

The rapidly growing population, climate crisis, and food security issues are increasingly highlighting the importance of digitalization and product tracking technologies in agricultural production. It is well-known that digitalization provides significant contributions to critical areas such as food safety, sustainability, and efficiency. In particular, product tracking technologies hold great potential for enhancing efficiency, optimizing resource use, and reducing environmental impacts. In this study, product tracking technologies are examined under the categories of automation and robotics, imaging and sensors, big data, and data analytics. With the widespread use in advanced technologies such as GPS, IoT, and artificial intelligence in the agricultural sector, it has been demonstrated that agricultural processes have become more efficient, and traceability and transparency have been ensured from production to the consumer. Furthermore, it is anticipated that the application of digital twin technology in agriculture in the near future will enhance agricultural productivity and enable more sustainable production.

Anahtar Kelimeler

Crop Monitoring Automation and Robotics Imaging and Sensors Big Data and Data Analytics

Kaynakça

  • Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H. S., ve Radiom, S. (2018). Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4563-4577. https://doi.org/10.1109/JSTARS.2018.2823361
  • Ahmed, I., Ahmad, M., Ghazouani, H., Barhoumi, W., ve Jeon, G. (2025). Intelligent Computing for Crop Monitoring in CIoT : Leveraging AI and Big Data Technologies. Expert Systems, 42(2), 1-14. https://doi.org/10.1111/exsy.13786
  • Ali, A. M., Abouelghar, M., Belal, A. A., Saleh, N., Yones, M., Selim, A. I., Amin, M. E. S., Elwesemy, A., Kucher, D. E., Maginan, S., ve Savin, I. (2022). Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article). The Egyptian Journal of Remote Sensing and Space Science, 25(3), 711- 716. https://doi.org/10.1016/j.ejrs.2022.04.006
  • Aria, M., ve Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Aydın Eryılmaz, G., ve Kılıç, O. (2018). Türkiye’de Sürdürülebilir Tarım ve İyi Tarım Uygulamaları. Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi, 21(4), 624-631. https://doi.org/10.18016/ksudobil.345137
  • Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T., ve Gómez-Barbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, (80), 163-174. https://doi.org/10.1016/j.landusepol.2018.10.004
  • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., ve Bareth, G. (2014). Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing, 6(11), 10395- 10412. https://doi.org/10.3390/rs61110395
  • Birner, R., Daum, T., ve Pray, C. (2021). Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges. Applied Economic Perspectives and Policy, 43(4), 1260-1285. https://doi.org/10.1002/aepp.13145
  • Cesco, S., Sambo, P., Borin, M., Basso, B., Orzes, G., ve Mazzetto, F. (2023). Smart agriculture and digital twins: Applications and challenges in a vision of sustainability. European Journal of Agronomy, (146), 126809. https://doi.org/10.1016/j.eja.2023.126809
  • Chergui, N., ve Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1), 123. https://doi.org/10.1186/s40537-022-00668-2
  • Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., ve Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, (219), 145-161. https://doi.org/10.1016/j.rse.2018.09.002
  • Cuaran, J., ve Leon, J. (2021). Crop Monitoring using Unmanned Aerial Vehicles: A Review. Agricultural Reviews, 42(2), 121-132. https://doi.org/10.18805/ag.R-180
  • Deichmann, U., Goyal, A., ve Mishra, D. (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1), 21-33. https://doi.org/10.1111/agec.12300
  • Ennouri, K., Smaoui, S., Gharbi, Y., Cheffi, M., Ben Braiek, O., Ennouri, M., ve Triki, M. A. (2021). Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields. Journal of Food Quality, (2021), 1-17. https://doi.org/10.1155/2021/6242288
  • Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., ve Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20(9), 1-27. https://doi.org/10.3390/s20092672
  • Garcia-Sanchez, A.-J., Garcia-Sanchez, F., ve Garcia-Haro, J. (2011). Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture, 75(2), 288-303. https://doi.org/10.1016/j.compag.2010.12.005
  • Gomiero, T. (2019). Saving Food Production, Supply Chain, Food Waste and Food Consumption İçinde C. M. Galanakis (Ed.) Soil and crop management to save food and enhance food security (ss. 33-87). Academic Press. https://doi.org/10.1016/B978-0-12-815357-4.00002-X
  • Hasan, M., Uddin, K. N. W., Sayeed, A., ve Tasneem, T. (2021). Smart Agriculture Robotic System Based on Internet of Things to Boost Crop Production. 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 157-162. https://doi.org/10.1109/ICREST51555.2021.9331091
  • Javaid, M., Haleem, A., Khan, I. H., ve Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001
  • Karmakar, P., Teng, S. W., Murshed, M., Pang, S., Li, Y., ve Lin, H. (2024). Crop monitoring by multimodal remote sensing: A review. Remote Sensing Applications: Society and Environment, 33, 101093. https://doi.org/10.1016/j.rsase.2023.101093
  • Kum, S., Oh, S., ve Moon, J. (2024). Edge AI Framework for Large Scale Smart Agriculture. 27th Conference on Innovation in Clouds, Internet and Networks (ICIN), 143-147. https://doi.org/10.1109/ICIN60470.2024.10494451
  • Kumar, P., Singh, A., Rajput, V. D., Yadav, A. K. S., Kumar, P., Singh, A. K., ve Minkina, T. (2022). Bioinformatics in Agriculture Next Generation Sequencing Era İçinde P. Sharma, D. Yadav and R. K. Gaur (Ed.), Role of artificial intelligence, sensor technology, big data in agriculture: next- generation farming (ss. 625-639). Academic Press. https://doi.org/10.1016/B978-0-323-89778-5.00035-0
  • Le Mouël, C., ve Forslund, A. (2017). How can we feed the world in 2050? A review of the responses from global scenario studies. European Review of Agricultural Economics, 44(4), 541-591. https://doi.org/10.1093/erae/jbx006
  • Markets and Markets. (2020). Crop Monitoring Market with Covid-19 Impact - Global Forecast to 2025. https://www.marketsandmarkets.com/Market-Reports/crop- monitoring-market-8994590.html Erişim Tarihi: 19.05.2025
  • Marques, L. S., Ferraz, G. A. e S., Moreira Neto, J., Magalhães, R. R., de Lima, D. A., Tsuchida, J. E., ve Fuzatto, D. C. (2022). Agricultural Machinery Telemetry: A Bibliometric Analysis. AgriEngineering, 4(4), 939-950. https://doi.org/10.3390/agriengineering4040060
  • Milella, A., Rilling, S., Rana, A., Galati, R., Petitti, A., Hoffmann, M., Stanly, J. L., ve Reina, G. (2024). Robot-as-a-Service as a New Paradigm in Precision Farming. IEEE Access, 12, 47942-47949. https://doi.org/10.1109/ACCESS.2024.3381511
  • Ndikumana, E., Ho Tong Minh, D., Dang Nguyen, H. T., Baghdadi, N., Courault, D., Hossard, L., ve El Moussawi, I. (2018). Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sensing, 10(9), 1394. https://doi.org/10.3390/rs10091394
  • Olson, D., ve Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 113(2), 971-992. https://doi.org/10.1002/agj2.20595
  • O’Sullivan, J. N. (2023). Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures. World, 4(3), 545-568. https://doi.org/10.3390/world4030034
  • Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., ve Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. https://doi.org/10.1016/j.compag.2022.107096
  • Research And Markets. (2025). Europe Precision Agriculture Market: Focus on Application, Product, and Country - Analysis and Forecast, 2024-2034. https://www.researchandmarkets.com/report/europe- precision-agriculture-market Erişim Tarihi: 19.05.2025
  • Sishodia, R. P., Ray, R. L., ve Singh, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
  • Story, D., Kacira, M., Kubota, C., Akoglu, A., ve An, L. (2010). Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture, 74(2), 238-243. https://doi.org/10.1016/j.compag.2010.08.010
  • T.C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2019). On Birinci Kalkınma Planı (2019-2023). https://www.sbb.gov.tr/wp- content/uploads/2022/07/On_Birinci_Kalkinma_Plani-2019-2023.pdf Erişim Tarihi: 19.05.2025
  • T.C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2023). On İkinci Kalkınma Planı (2024-2028). https://www.sbb.gov.tr/wp- content/uploads/2023/12/On-Ikinci-Kalkinma-Plani_2024- 2028_11122023.pdf Erişim Tarihi: 19.05.2025
  • T.C. Kalkınma Bakanlığı. (2013). Onuncu Kalkınma Planı (2014-2018). https://www.sbb.gov.tr/wp- content/uploads/2022/08/Onuncu_Kalkinma_Plani-2014-2018.pdf Erişim Tarihi: 19.05.2025
  • Torres-Torriti, M., ve Burgos, P. N. (2023). Encyclopedia of Digital Agricultural Technologies İçinde Q. Zhang (Ed.), Field Machinery Automated Guidance (ss. 509-526). Springer International Publishing. https://doi.org/10.1007/978-3-031-24861-0_229
  • van Dinter, R., Tekinerdogan, B., ve Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
  • Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.-F., ve Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415-426. https://doi.org/10.1016/j.rse.2017.07.015
  • Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., ve Hawkesford, M. J. (2017). Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology, 44(1), 143. https://doi.org/10.1071/FP16163
  • Wei, Y., Lee, K., ve Lee, K. (2024). Autonomous Field Navigation of Mobile Robots for Data Collection and Monitoring in Agricultural Crop Fields. 21st International Conference on Ubiquitous Robots (UR), 707-712. https://doi.org/10.1109/UR61395.2024.10597470
  • Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., Zheng, Y., Zhang, N., Chang, S., Xing, Q., ve Van Heijden, A. (2015). Global Crop Monitoring: A Satellite-Based Hierarchical Approach. Remote Sensing, 7(4), 3907-3933. https://doi.org/10.3390/rs70403907

Bitkisel Tarımda Kullanılan Ürün İzleme Teknolojilerinin İncelenmesi

Yıl 2025, Cilt: 15 Sayı: 1, 132 - 145, 26.06.2025

Bora Aslan , Füsun Yavuzer Aslan

https://doi.org/10.53518/mjavl.1599535

Öz

Hızla artan nüfus, iklim krizi ve gıda güvenliği sorunları, tarımsal üretimde dijitalleşme ve ürün izleme teknolojilerinin önemini her geçen gün artırmaktadır. Dijitalleşmenin, gıda güvenliği, sürdürülebilirlik ve verimlilik gibi kritik alanlara önemli katkılar sağladığı bilinmektedir. Özellikle ürün izleme teknolojileri, verimliliği artırma, kaynak kullanımını optimize etme ve çevresel etkileri azaltma açısından büyük bir potansiyele sahiptir. Bu çalışmada, ürün izleme teknolojileri; otomasyon ve robotik, görüntüleme ve sensörler, büyük veri ve veri analitiği başlıkları altında incelenmiştir. GPS, IoT ve yapay zeka gibi ileri teknolojilerin tarım sektöründe kullanımının yaygınlaşmasıyla birlikte, tarımsal süreçlerin daha verimli hale geldiği, üretimden tüketiciye kadar izlenebilirliğin ve şeffaflığın sağlandığı ortaya konulmuştur. Ayrıca, yakın gelecekte dijital ikiz teknolojisinin tarımda uygulanmasıyla tarımsal verimliliğin artacağı ve daha sürdürülebilir bir üretim yapılacağı öngörülmektedir.

Anahtar Kelimeler

Ürün İzleme Otomasyon ve Robotik Görüntüleme ve Sensörler Büyük Veri ve Veri Analitiği

Kaynakça

  • Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H. S., ve Radiom, S. (2018). Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4563-4577. https://doi.org/10.1109/JSTARS.2018.2823361
  • Ahmed, I., Ahmad, M., Ghazouani, H., Barhoumi, W., ve Jeon, G. (2025). Intelligent Computing for Crop Monitoring in CIoT : Leveraging AI and Big Data Technologies. Expert Systems, 42(2), 1-14. https://doi.org/10.1111/exsy.13786
  • Ali, A. M., Abouelghar, M., Belal, A. A., Saleh, N., Yones, M., Selim, A. I., Amin, M. E. S., Elwesemy, A., Kucher, D. E., Maginan, S., ve Savin, I. (2022). Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article). The Egyptian Journal of Remote Sensing and Space Science, 25(3), 711- 716. https://doi.org/10.1016/j.ejrs.2022.04.006
  • Aria, M., ve Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Aydın Eryılmaz, G., ve Kılıç, O. (2018). Türkiye’de Sürdürülebilir Tarım ve İyi Tarım Uygulamaları. Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi, 21(4), 624-631. https://doi.org/10.18016/ksudobil.345137
  • Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T., ve Gómez-Barbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, (80), 163-174. https://doi.org/10.1016/j.landusepol.2018.10.004
  • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., ve Bareth, G. (2014). Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing, 6(11), 10395- 10412. https://doi.org/10.3390/rs61110395
  • Birner, R., Daum, T., ve Pray, C. (2021). Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges. Applied Economic Perspectives and Policy, 43(4), 1260-1285. https://doi.org/10.1002/aepp.13145
  • Cesco, S., Sambo, P., Borin, M., Basso, B., Orzes, G., ve Mazzetto, F. (2023). Smart agriculture and digital twins: Applications and challenges in a vision of sustainability. European Journal of Agronomy, (146), 126809. https://doi.org/10.1016/j.eja.2023.126809
  • Chergui, N., ve Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1), 123. https://doi.org/10.1186/s40537-022-00668-2
  • Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., ve Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, (219), 145-161. https://doi.org/10.1016/j.rse.2018.09.002
  • Cuaran, J., ve Leon, J. (2021). Crop Monitoring using Unmanned Aerial Vehicles: A Review. Agricultural Reviews, 42(2), 121-132. https://doi.org/10.18805/ag.R-180
  • Deichmann, U., Goyal, A., ve Mishra, D. (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1), 21-33. https://doi.org/10.1111/agec.12300
  • Ennouri, K., Smaoui, S., Gharbi, Y., Cheffi, M., Ben Braiek, O., Ennouri, M., ve Triki, M. A. (2021). Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields. Journal of Food Quality, (2021), 1-17. https://doi.org/10.1155/2021/6242288
  • Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., ve Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20(9), 1-27. https://doi.org/10.3390/s20092672
  • Garcia-Sanchez, A.-J., Garcia-Sanchez, F., ve Garcia-Haro, J. (2011). Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture, 75(2), 288-303. https://doi.org/10.1016/j.compag.2010.12.005
  • Gomiero, T. (2019). Saving Food Production, Supply Chain, Food Waste and Food Consumption İçinde C. M. Galanakis (Ed.) Soil and crop management to save food and enhance food security (ss. 33-87). Academic Press. https://doi.org/10.1016/B978-0-12-815357-4.00002-X
  • Hasan, M., Uddin, K. N. W., Sayeed, A., ve Tasneem, T. (2021). Smart Agriculture Robotic System Based on Internet of Things to Boost Crop Production. 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 157-162. https://doi.org/10.1109/ICREST51555.2021.9331091
  • Javaid, M., Haleem, A., Khan, I. H., ve Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001
  • Karmakar, P., Teng, S. W., Murshed, M., Pang, S., Li, Y., ve Lin, H. (2024). Crop monitoring by multimodal remote sensing: A review. Remote Sensing Applications: Society and Environment, 33, 101093. https://doi.org/10.1016/j.rsase.2023.101093
  • Kum, S., Oh, S., ve Moon, J. (2024). Edge AI Framework for Large Scale Smart Agriculture. 27th Conference on Innovation in Clouds, Internet and Networks (ICIN), 143-147. https://doi.org/10.1109/ICIN60470.2024.10494451
  • Kumar, P., Singh, A., Rajput, V. D., Yadav, A. K. S., Kumar, P., Singh, A. K., ve Minkina, T. (2022). Bioinformatics in Agriculture Next Generation Sequencing Era İçinde P. Sharma, D. Yadav and R. K. Gaur (Ed.), Role of artificial intelligence, sensor technology, big data in agriculture: next- generation farming (ss. 625-639). Academic Press. https://doi.org/10.1016/B978-0-323-89778-5.00035-0
  • Le Mouël, C., ve Forslund, A. (2017). How can we feed the world in 2050? A review of the responses from global scenario studies. European Review of Agricultural Economics, 44(4), 541-591. https://doi.org/10.1093/erae/jbx006
  • Markets and Markets. (2020). Crop Monitoring Market with Covid-19 Impact - Global Forecast to 2025. https://www.marketsandmarkets.com/Market-Reports/crop- monitoring-market-8994590.html Erişim Tarihi: 19.05.2025
  • Marques, L. S., Ferraz, G. A. e S., Moreira Neto, J., Magalhães, R. R., de Lima, D. A., Tsuchida, J. E., ve Fuzatto, D. C. (2022). Agricultural Machinery Telemetry: A Bibliometric Analysis. AgriEngineering, 4(4), 939-950. https://doi.org/10.3390/agriengineering4040060
  • Milella, A., Rilling, S., Rana, A., Galati, R., Petitti, A., Hoffmann, M., Stanly, J. L., ve Reina, G. (2024). Robot-as-a-Service as a New Paradigm in Precision Farming. IEEE Access, 12, 47942-47949. https://doi.org/10.1109/ACCESS.2024.3381511
  • Ndikumana, E., Ho Tong Minh, D., Dang Nguyen, H. T., Baghdadi, N., Courault, D., Hossard, L., ve El Moussawi, I. (2018). Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sensing, 10(9), 1394. https://doi.org/10.3390/rs10091394
  • Olson, D., ve Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 113(2), 971-992. https://doi.org/10.1002/agj2.20595
  • O’Sullivan, J. N. (2023). Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures. World, 4(3), 545-568. https://doi.org/10.3390/world4030034
  • Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., ve Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. https://doi.org/10.1016/j.compag.2022.107096
  • Research And Markets. (2025). Europe Precision Agriculture Market: Focus on Application, Product, and Country - Analysis and Forecast, 2024-2034. https://www.researchandmarkets.com/report/europe- precision-agriculture-market Erişim Tarihi: 19.05.2025
  • Sishodia, R. P., Ray, R. L., ve Singh, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
  • Story, D., Kacira, M., Kubota, C., Akoglu, A., ve An, L. (2010). Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture, 74(2), 238-243. https://doi.org/10.1016/j.compag.2010.08.010
  • T.C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2019). On Birinci Kalkınma Planı (2019-2023). https://www.sbb.gov.tr/wp- content/uploads/2022/07/On_Birinci_Kalkinma_Plani-2019-2023.pdf Erişim Tarihi: 19.05.2025
  • T.C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2023). On İkinci Kalkınma Planı (2024-2028). https://www.sbb.gov.tr/wp- content/uploads/2023/12/On-Ikinci-Kalkinma-Plani_2024- 2028_11122023.pdf Erişim Tarihi: 19.05.2025
  • T.C. Kalkınma Bakanlığı. (2013). Onuncu Kalkınma Planı (2014-2018). https://www.sbb.gov.tr/wp- content/uploads/2022/08/Onuncu_Kalkinma_Plani-2014-2018.pdf Erişim Tarihi: 19.05.2025
  • Torres-Torriti, M., ve Burgos, P. N. (2023). Encyclopedia of Digital Agricultural Technologies İçinde Q. Zhang (Ed.), Field Machinery Automated Guidance (ss. 509-526). Springer International Publishing. https://doi.org/10.1007/978-3-031-24861-0_229
  • van Dinter, R., Tekinerdogan, B., ve Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
  • Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.-F., ve Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415-426. https://doi.org/10.1016/j.rse.2017.07.015
  • Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., ve Hawkesford, M. J. (2017). Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology, 44(1), 143. https://doi.org/10.1071/FP16163
  • Wei, Y., Lee, K., ve Lee, K. (2024). Autonomous Field Navigation of Mobile Robots for Data Collection and Monitoring in Agricultural Crop Fields. 21st International Conference on Ubiquitous Robots (UR), 707-712. https://doi.org/10.1109/UR61395.2024.10597470
  • Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., Zheng, Y., Zhang, N., Chang, S., Xing, Q., ve Van Heijden, A. (2015). Global Crop Monitoring: A Satellite-Based Hierarchical Approach. Remote Sensing, 7(4), 3907-3933. https://doi.org/10.3390/rs70403907
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tarım Sistemleri Analizi ve Modellemesi, Tarımsal Üretim Sistemleri Simulasyonu
Bölüm Derleme
Yazarlar

Bora Aslan 0000-0002-8069-8204

Füsun Yavuzer Aslan 0000-0001-7096-3425

Yayımlanma Tarihi 26 Haziran 2025
Gönderilme Tarihi 10 Aralık 2024
Kabul Tarihi 26 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA Aslan, B., & Yavuzer Aslan, F. (2025). Bitkisel Tarımda Kullanılan Ürün İzleme Teknolojilerinin İncelenmesi. Manas Journal of Agriculture Veterinary and Life Sciences, 15(1), 132-145. https://doi.org/10.53518/mjavl.1599535
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