Dijital Tarım, Tarım 4.0, Akılı Tarım, Robotik Uygulamalar ve Otonom Sistemler

Yıl 2022, Cilt: 18 Sayı: 2, 68 - 83, 30.10.2022

Hasan Şahin

Öz

Savaşlar, iklim değişikliği, salgın hastalıklar, kaçınılmaz politik göçler, dünya nüfusunun artması, nüfusun kırsal alanlardan şehirlere göçü ve yaşlanan nüfus, gıda ihtiyacının artmasına neden olmaktadır. Tarımda işçilik maliyetlerinin artışı, tarımsal faaliyetlerin fiziksel zorluğu ve tekrarlanan işlerden oluşması, tarımda mekatronik ve robotik uygulamaların artmasına neden olmuştur. Robotik ve mekatronik uygulamalar, tarımsal ürün tedarikinde verimliliği artırmakla birlikte, sosyal ve çevresel faydalar da sağlamaktadır. Pestisit ve herbisit kullanımını engelleyen robotik yabancı ot ayıklama uygulamaları ve hassas püskürtücü sistemler gibi uygulamalarda doğrudan pozitif çevresel bir etki saplamaktadır. Çalışmada, Dijital Tarım, Tarım 4.0, Akıllı Tarım, Tarımsal Robotik ve Otonom Sistemler ile ilgili yakın zamanda yayımlanmış olan literatür taraması yapılarak, teorik, laboratuvar ve saha uygulamaları içeren makaleler incelenmiştir. Bu çalışmada, dünyada, son on yılda dijital/akıllı/robotik tarımda yükselen trendler, bu alanda karşılaşılan temel zorluklar ve geleceğin tarımsal uygulanmalarını destekleyecek kurumlar arası ortak bir stratejinin nasıl geliştirilebileceğine dair sorulara cevap aranmıştır. Dijital tarım, akıllı tarım, robotik tarım, tarım 4.0, hassas tarım gibi birçok kavramın kullanıldığı bir dönemde, kurumlar arası bir iş birliği ve iş bölümüne ihtiyaç duyulduğu görülmektedir. Ulusal anlamda ise, kısa, orta ve uzun vadeli stratejiler belirlenerek üniversiteler, tarım bakanlığı ve TÜBİTAK gibi kurumlar arası iş bölümü yapılması bilgi kirliliği ve kavram kargaşasının önüne geçerek zaman kaybını azaltacaktır.

Anahtar Kelimeler

Dijital tarım, Tarımsal robotik, Otonom sistemler

Kaynakça

• Abdul Jabbar, K., Hansen, M. F., Smith, M. L., & Smith, L. N. (2017). Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosystems Engineering, 153. https://doi.org/10.1016/j.biosystemseng.2016.09.017
• Abouzıena, H. F., & Haggag, W. M. (2016). Weed Control in Clean Agriculture: A Review1. Planta Daninha, 34(2). https://doi.org/10.1590/s0100-83582016340200019
• Aggelopoulou, A. D., Bochtis, D., Fountas, S., Swain, K. C., Gemtos, T. A., & Nanos, G. D. (2011). Yield prediction in apple orchards based on image processing. Precision Agriculture, 12(3). https://doi.org/10.1007/s11119-010-9187-0
• Ahmad, L., & Nabi, F. (2021). Smart Intelligent Precision Agriculture. In Agriculture 5.0: Artificial Intelligence, IoT, and Machine Learning (pp. 25-34). CRC Press.Ahmad, L., & Nabi, F. (2021). Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning.
• Akbaş, G. G., & Bağcı, A. (2021). Economic growth and smart farming. Gazi İktisat ve İşletme Dergisi, 7(2), 104–121. https://doi.org/10.30855/GJEB.2021.7.2.002
• Akella, P., Peshkin, M., Colgate, E., Wannasuphoprasit, W., Nagesh, N., Wells, J., Holland, S., Pearson, T., & Peacock, B. (1999). Cobots for the automobile assembly line. Proceedings - IEEE International Conference on Robotics and Automation, 1. https://doi.org/10.1109/robot.1999.770061
• Al Abeach, L. A. T., Nefti-Meziani, S., & Davis, S. (2017). Design of a Variable Stiffness Soft Dexterous Gripper. Soft Robotics, 4(3). https://doi.org/10.1089/soro.2016.0044
• Amend, S., Brandt, D., Di Marco, D., Dipper, T., Gässler, G., Höferlin, M., Gohlke, M., Kesenheimer, K., Lindner, P., Leidenfrost, R., Michaels, A., Mugele, T., Müller, A., Riffel, T., Sampangi, Y., & Winkler, J. (2019). Weed Management of the Future. KI - Kunstliche Intelligenz, 33(4). https://doi.org/10.1007/s13218-019-00617-x
• Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape—emerging trends, challenges and opportunities. In Agronomy (Vol. 11, Issue 4). MDPI AG. https://doi.org/10.3390/agronomy11040667
• Bac, C. W., Van Henten, E. J., Hemming, J., & Edan, Y. (2014). Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead. In Journal of Field Robotics (Vol. 31, Issue 6). https://doi.org/10.1002/rob.21525
• B. Şin, İ. Kadıoğlu. (2019). İnsansız Hava Aracı (İHA) ve Görüntü İşleme Teknikleri Kullanılarak Yabancı Ot Tespitinin Yapılması. Retrieved March 11, 2022, from https://dergipark.org.tr/en/pub/tjws/issue/51404/669501
• Barnes, M., Duckett, T., Cielniak, G., Stroud, G., & Harper, G. (2010). Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering, 98(3). https://doi.org/10.1016/j.jfoodeng.2010.01.010
• Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. In Biosystems Engineering (Vol. 149). https://doi.org/10.1016/j.biosystemseng.2016.06.014
• Bhardwaj, H., Tomar, P., (2021). Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques. Igi-Global.Com.
• Blackmore, S. (2009). New concepts in agricultural automation. Precision in Arable Farming: Current Practice and Future Potential, October.
• Bosilj, P., Duckett, T., & Cielniak, G. (2018). Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture. Computers in Industry, 98. https://doi.org/10.1016/j.compind.2018.02.003
• Brodie, G. (2018). The use of physics in weed control. In Non-Chemical Weed Control. https://doi.org/10.1016/B978-0-12-809881-3.00003-6
• Cheng, Y. C., Lu, H. C., Lee, X., Zeng, H., & Priimagi, A. (2020). Kirigami-Based Light-Induced Shape-Morphing and Locomotion. Advanced Materials, 32(7). https://doi.org/10.1002/adma.201906233
• Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., & Fraisse, P. (2016). Collaborative manufacturing with physical human-robot interaction. Robotics and Computer-Integrated Manufacturing, 40. https://doi.org/10.1016/j.rcim.2015.12.007
• Coleman, G., Betters, C., Squires, C., Leon-Saval, S., & Walsh, M. (2021). Low Energy Laser Treatments Control Annual Ryegrass (Lolium rigidum). Frontiers in Agronomy, 2. https://doi.org/10.3389/fagro.2020.601542
• Coleman, G. R. Y., Stead, A., Rigter, M. P., Xu, Z., Johnson, D., Brooker, G. M., Sukkarieh, S., & Walsh, M. J. (2019). Using energy requirements to compare the suitability of alternative methods for broadcast and site-specific weed control. In Weed Technology (Vol. 33, Issue 4). https://doi.org/10.1017/wet.2019.32
• Dasgupta, I., Saha, J., Venkatasubbu, P., & Ramasubramanian, P. (2020). AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers. Arabian Journal for Science and Engineering, 45(12). https://doi.org/10.1007/s13369-020-04928-2
• Dayıoğlu, M. A., & Turker, U. (2021). Digital Transformation for Sustainable Future - Agriculture 4.0 : A review. Tarım Bilimleri Dergisi, 27(4), 373–399. https://doi.org/10.15832/ankutbd.986431
• Dos Santos Ferreira, A., Matte Freitas, D., Gonçalves da Silva, G., Pistori, H., & Theophilo Folhes, M. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143. https://doi.org/10.1016/j.compag.2017.10.027
• Ercan, Ş., Öztep, R., Güler, D., & Saner, G. (2019). Tarım 4.0 ve Türkiye’de Uygulanabilirliğinin Değerlendirilmesi. Tarım Ekonomisi Dergisi, 25(2), 259–265. https://doi.org/10.24181/Tarekoder.650762
• Fentanes, J., Gould, I., (2018). 3-d soil compaction mapping through kriging-based exploration with a mobile robot. Ieeexplore.Ieee.Org.
• Fernández-Novales, J., Saiz-Rubio, V., Barrio, I., Rovira-Más, F., Cuenca-Cuenca, A., Santos Alves, F., Valente, J., Tardáguila, J., & Diago, M. P. (2021). Monitoring and mapping vineyard water status using non-invasive technologies by a ground robot. Remote Sensing, 13(14). https://doi.org/10.3390/rs13142830
• Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors (Switzerland), 17(9). https://doi.org/10.3390/s17092022
• Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., ... & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741.
• Ghanizadeh, H., & Harrington, K. C. (2019). Weed management in New Zealand pastures. In Agronomy (Vol. 9, Issue 8). https://doi.org/10.3390/agronomy9080448
• Godaba, H., Sajad, A., Patel, N., Althoefer, K., & Zhang, K. (2020). A two-fingered robot gripper with variable stiffness flexure hinges based on shape morphing. IEEE International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/IROS45743.2020.9341554
• Grimstad, L., & From, P. J. (2017). The Thorvald II agricultural robotic system. Robotics, 6(4). https://doi.org/10.3390/robotics6040024 Haddadin, S., & Croft, E. (2016). Physical human-robot interaction. Springer Handbook of Robotics, 1835–1874. https://doi.org/10.1007/978-3-319-32552-1_69
• Hansen, M. F., Smith, M. L., Smith, L. N., Abdul Jabbar, K., & Forbes, D. (2018). Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Computers in Industry, 98. https://doi.org/10.1016/j.compind.2018.02.011
• Hansen, M. F., Smith, M. L., Smith, L. N., Salter, M. G., Baxter, E. M., Farish, M., & Grieve, B. (2018). Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry, 98. https://doi.org/10.1016/j.compind.2018.02.016
• Haug, S., Michaels, A., Biber, P., & Ostermann, J. (2014). Plant classification system for crop /weed discrimination without segmentation. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. https://doi.org/10.1109/WACV.2014.6835733
• Hess, M. C. M., De Wilde, M., Yavercovski, N., Willm, L., Mesléard, F., & Buisson, E. (2018). Microwave soil heating reduces seedling emergence of a wide range of species including invasives. Restoration Ecology. https://doi.org/10.1111/rec.12668
• Hitz, G., Pomerleau, F., Garneau, M. È., Pradalier, C., Posch, T., Pernthaler, J., & Siegwart, R. Y. (2012). Autonomous inland water monitoring: Design and application of a surface vessel. IEEE Robotics and Automation Magazine, 19(1). https://doi.org/10.1109/MRA.2011.2181771
• International Federation of Robotics. (2017). Executive Summary—World Robotics (Service Robots) 2017. World Robotic Report–Executive Summary.
• Jabir, B., & Falih, N. (2022). Deep learning-based decision support system for weeds detection in wheat fields. International Journal of Electrical and Computer Engineering, 12(1). https://doi.org/10.11591/ijece.v12i1.pp816-825
• Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., & Anwar, S. (2021). Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precision Agriculture, 22(6). https://doi.org/10.1007/s11119-021-09808-9
• Korinek, A., & Stiglitz, J. E. (2021). Artificial Intelligence, Globalization, and Strategies for Economic Development.
• Kumar, S. P., Tewari, V. K., Chandel, A. K., Mehta, C. R., Nare, B., Chethan, C. R., Mundhada, K., Shrivastava, P., Gupta, C., & Hota, S. (2020). A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds. Artificial Intelligence in Agriculture, 4, 116–126. https://doi.org/10.1016/J.AIIA.2020.06.004
• Kusumam, K., Krajník, T., Pearson, S., Duckett, T., & Cielniak, G. (2017). 3D-vision based detection, localization, and sizing of broccoli heads in the field. Journal of Field Robotics, 34(8). https://doi.org/10.1002/rob.21726
• Liu, B., & Bruch, R. (2020). Weed Detection for Selective Spraying: a Review. Current Robotics Reports, 1(1). https://doi.org/10.1007/s43154-020-00001-w
• Liu, J., & Wang, X. (2020). Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.00898
• Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (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
• Lottes, P., Hörferlin, M., Sander, S., & Stachniss, C. (2017). Effective Vision-based Classification for Separating Sugar Beets and Weeds for Precision Farming. Journal of Field Robotics, 34(6). https://doi.org/10.1002/rob.21675
• Luiz Carlos, M., & Ulson, J. A. C. (2021). Real time weed detection using computer vision and deep learning. 2021 14th IEEE International Conference on Industry Applications, INDUSCON 2021 - Proceedings. https://doi.org/10.1109/INDUSCON51756.2021.9529761
• Machleb, J., Peteinatos, G. G., Kollenda, B. L., Andújar, D., & Gerhards, R. (2020). Sensor-based mechanical weed control: Present state and prospects. In Computers and Electronics in Agriculture (Vol. 176). https://doi.org/10.1016/j.compag.2020.105638
• Martins, B. H., Araujo-Junior, C. F., Miyazawa, M., Vieira, K. M., & Milori, D. M. B. P. (2015). Soil organic matter quality and weed diversity in coffee plantation area submitted to weed control and cover crops management. Soil and Tillage Research, 153. https://doi.org/10.1016/j.still.2015.06.005
• Marx, C., Barcikowski, S., Hustedt, M., Haferkamp, H., & Rath, T. (2012). Design and application of a weed damage model for laser-based weed control. Biosystems Engineering, 113(2). https://doi.org/10.1016/j.biosystemseng.2012.07.002
• Mathiassen, S. K., Bak, T., Christensen, S., & 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
• Xiaoyu, L. (2020, December). Application and research of artificial intelligence in mechatronic engineering. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 235-238). IEEE.
• Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity {\textbar} {Science} {Advances}. Sci Adv, 2(2).
• Ngugi, L. C., Abelwahab, M., & Abo-Zahhad, M. (2021). Recent advances in image processing techniques for automated leaf pest and disease recognition – A review. In Information Processing in Agriculture (Vol. 8, Issue 1). https://doi.org/10.1016/j.inpa.2020.04.004
• Olsen, D. R., & Wood, S. B. (2004). Fan-out: Measuring human control of multiple robots. Conference on Human Factors in Computing Systems - Proceedings.
• Özgen, H., & Turan, M. (2020). Sulama/İlaçlama Robotu için Nesne Tanıma Çalışmaları. Avrupa Bilim ve Teknoloji Dergisi, 50–58. https://doi.org/10.31590/EJOSAT.779052
• Pakdemirli, B., Birişik, N., Aslan, İ., Sönmez, B., Gezici, M., Tarım, T. C., Bakanı, O., Araştırmalar Ve Politikalar, T., Müdürlüğü, G., & Yazar, S. (2021). Türk Tarımında Dijital Teknolojilerin Kullanımı ve Tarım-Gıda Zincirinde Tarım 4.0. Toprak Su Dergisi, 10(1), 78–87. https://doi.org/10.21657/TOPRAKSU.898774
• Pereira, A., Science, M. A.-I. T. (2017). Overapproximative human arm occupancy prediction for collision avoidance. Ieeexplore.Ieee.Org.
• Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., Pridmore, T. P., & French, A. P. (2018). Erratum: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping [GigaScience, 6, 10] DOI: 10.1093/gigascience/gix083. In GigaScience (Vol. 7, Issue 7). https://doi.org/10.1093/gigascience/giy042
• Rahman, M., Blackwell, B., Banerjee, N., & Saraswat, D. (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture, 113, 14–23. https://doi.org/10.1016/J.COMPAG.2014.12.012
• Russell, S., Foundations, P. N. (2021). Artificial Intelligence: A Modern Approach, Global Edition 4th. Elibrary.Pearson.De.
• Sabancı, K., & Aydın, C. (2014). Görüntü İşleme Tabanlı Hassas İlaçlama Robotu. Tarım Bilimleri Dergisi, 20(4), 406. https://doi.org/10.15832/tbd.33629
• Sahin, H. (2014). Effects of Microwaves on the Germination of Weed Seeds. Journal of Biosystems Engineering, 39(4), 304–309. https://doi.org/10.5307/JBE.2014.39.4.304
• Sahin, H. (2020). Investigating the effect of single and multiple electrodes on mortality ratio in electric current weed control method with NDVI technique. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(4), 1973–1984. https://doi.org/10.17341/gazimmfd.698307
• Sahin, H., & Saglam, R. (2015). ARPN Journal of Agricultural and Biological Science A Research About Mıcrowave Effects On The Weed Plants. 10(3).
• Sahin, H., & Yalınkılıc, M. (2017). Using Electric Current as a Weed Control Method. European Journal of Engineering Research and Science. https://doi.org/10.24018/ejers.2017.2.6.379
• Saiz-Rubio, V., Agronomy, F. R.-M.-, & 2020, undefined. (n.d.). From smart farming towards agriculture 5.0: A review on crop data management. Mdpi.Com.
• Selvaraj, M. G., Vergara, A., Ruiz, H., Safari, N., Elayabalan, S., Ocimati, W., & Blomme, G. (2019). AI-powered banana diseases and pest detection. Plant Methods, 15(1). https://doi.org/10.1186/s13007-019-0475-z
• Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming. Int J Agric & Biol Eng, 11(4), 1–14. https://doi.org/10.25165/j.ijabe.20181104.4278
• Shepherd, R. F., Ilievski, F., Choi, W., Morin, S. a, Stokes, A. a, Mazzeo, A. D., Chen, X., Wang, M., & Whitesides, G. M. (2011). Multigait soft robot Supporting Information. Proceedings of the National Academy of Sciences of the United States of America, 108(51).
• Stanicek, B. (2020). BRIEFING EPRS | European Parliamentary Research Service. In Members’ Research Service PE (Vol. 689).
• Talaviya, T., Shah, D., Patel, N., Agriculture, H. Y., (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Elsevier.
• Tan, J. W., Chang, S. W., Abdul-Kareem, S., Yap, H. J., & Yong, K. T. (2020). Deep Learning for Plant Species Classification Using Leaf Vein Morphometric. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1). https://doi.org/10.1109/TCBB.2018.2848653
• Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation —A review. Information Processing in Agriculture, 7(1), 1–19. https://doi.org/10.1016/J.INPA.2019.09.006
• Tillett, N. D., Hague, T., Grundy, A. C., & Dedousis, A. P. (2008). Mechanical within-row weed control for transplanted crops using computer vision. Biosystems Engineering, 99(2). https://doi.org/10.1016/j.biosystemseng.2007.09.026
• Tim Chamen, W. C., Moxey, A. P., Towers, W., Balana, B., & Hallett, P. D. (2015). Mitigating arable soil compaction: A review and analysis of available cost and benefit data. Soil and Tillage Research, 146(PA). https://doi.org/10.1016/j.still.2014.09.011
• Tomar, P., & Kaur, G. (2021). Artificial Intelligence and IoT-based Technologies for Sustainable Farming and Smart Agriculture.
• Türkoğlu, M., & Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Sciences, 27(3). https://doi.org/10.3906/elk-1809-181
• Walter, A., Khanna, R., Lottes, P., Stachniss, C., Siegwart, R., Nieto, J., & Liebisch, F. (n.d.). Flourish-a robotic approach for automation in crop management. Ipb.Uni-Bonn.De. Wöltjen, C., Haferkamp, H., Rath, T., & Herzog, D. (2008). Plant growth depression by selective irradiation of the meristem with CO2 and diode lasers. Biosystems Engineering, 101(3). https://doi.org/10.1016/j.biosystemseng.2008.08.006
• Yaghoubi, S., Akbarzadeh, N., … S. B.-I. J. of, & 2013, undefined. (n.d.). Autonomous robots for agricultural tasks and farm assignment and future trends in agro robots. Citeseer.