Yabancı Otlar ile Mücadelede Güncel Yöntem: Robotikler

Year 2021, Volume: 24 Issue: 2, 166 - 176, 31.12.2021

Ender Şahin Çolak , Doğan Işık

Abstract

Dünya üzerinde tarımsal üretim, insanlığın var oluşundan itibaren, üretim metodları değişebilse de temelde insanların yaşamlarını sürdürme mücadelesidir. Tarımsal üretimde yetiştirilen üründen optimal verimi elde etmek asıl hedef olmaktadır. Yetiştirilen ürünlerde bir takım sorunlarla karşılaşılmakta ve sorunun boyutuna göre de elde edilen verim değişebilmektedir. Bitkisel üretimde olduğu gibi hayvansal üretimde de yabancı otlar verimin önündeki büyük bir engeli teşkil etmektedir. Yabancı otlar üstün rekabet güçleri ile verim kayıplarına sebebiyet verirler. Kültür bitkisinin yetişme aşamasında arazideki mevcut yabancı otlar var olan büyüme kaynaklarından daha etkin yararlanabilme özelliğine sahip olduğuklarından dolayı kültür bitkilerinden daha önce gelişimini tamamlayıp kültür bitkilerinin gelişimi için gerekli kaynaklara ulaşabilme imkanını kısıtlamaktadır. Tarımsal üreticiler tarafından çok eski tarihlerden bu yana herhangi bir verim kaybı olmaması için veya kayıpların minimize edilebilmesi için yabancı otlarla mücadele süregelmektedir. Yabancı otlarla elle mücadele şeklinde başlayan mücadele yöntemleri, sonraları teknolojide oluşan çeşitli gelişmelerle birlikte mekanik mücadele, fiziksel mücadele, kimyasal mücadele, biyolojik mücadele gibi çeşitli mücadele yöntemleri şeklinde uygulanmıştır. Bu yöntemler içerisinde en yaygını olanı işçilik ve ekonomik açıdan üreticiyi yormayan yöntem olan kimyasal mücadeledir. Ancak son yıllarda kimyasalların kullanımı sonucu yabancı otlarda direnç oluşması ve tüketicilerde halk sağlığı ve çevre konularında farkındalık oluşumu sebebiyle kimyasal kullanımından gelecekte uzaklaşılacağı düşünülmektedir. Bilim insanları günümüzde teknolojinin gelişimi ve kimyasallara alternatif yöntem arayışıyla robotik mücadeleye yönelmiştir. Robotikler ya hiç kimyasal kullanmayıp mekanik donanımlarıyla yabancı otların zararını önleyebilen ya da hedefe yönelik kimyasal püskürtme mekanizmasına sahip yabancı otlarla mücadele araçlarıdır. Robotikler kimyasallara alternatif olarak yabancı otlarla mücadelede kullanılmasının yanı sıra tarımsal üretimin bütün aşamalarında da kullanılabilmektedir. Bu derlemede yabancı otlarla mücadelede robotiklerin kullanılması ve robotik mücadele hakkında dünya üzerindeki gelişmeler incelenmiştir.

Keywords

Robotik, Kimyasal, Mücadele, Yabancı ot

Current Approach in Weed Control: Robotics

Abstract

Agricultural production on the world is basically the management of people to survive, although production methods may change since the existence of humanity. Obtaining optimal yield from the crop production in agricultural production is the main goal. A number of problems are encountered in the grown products and the yield obtained may vary depending on the size of the problem. As in crop production, weeds create a major obstacle to yield in animal production. Weeds cause yield losses with their high competitive power. During the growing stage of the cultivated plant, the existing weeds in the field have the ability to benefit more effectively from the existing growth resources, thus restricting the possibility of reaching the necessary resources for the development of the cultivated plants by completing their development earlier. The management of weeds has been ongoing since ancient times by agricultural producers in order to avoid any loss of yield or to minimize losses. Weed control methods, which started as manual control, were applied in the form of various control methods such as mechanical control, physical control, chemical control, biological control with various developments in technology. The most common of these methods is chemical control, which does not weak the producer in terms of labor and economy. However, it is thought that the use of chemicals will be avoided in the future due to resistance in weeds as a result of the use of chemicals and awareness of public health and environmental issues in consumers in recent years. Today, scientists have turned to robotic control with the development of technology and the search for alternative methods to chemicals. Robotics are weed control tools that either use no chemicals and can prevent weeds with their mechanical equipment or have a targeted chemical spraying mechanism. Robotics can be used as in the fight against weeds an alternative to chemicals as well as being used in all of process of agricultural production. In this review, the use of robotics in management of weeds and developments in the world about robotic control are examined.

Keywords

Robotics, Robotics, Chemical, Control, Weed

References

• Armstrong J. J. Q., Dirks, R. D., & Gibson, K. D. (2007). The use of early season multispectral images for weed detection in corn. Weed Technology, 21(4), 857-862.
• Bakker T., Wouters, H., Van Asselt, K., Bontsema, J., Tang, L., Müller, J., & van Straten, G. (2008). A vision based row detection system for sugar beet. Computers and electronics in agriculture, 60(1), 87-95.
• Bak T., Jakobsen, H. (2004). Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering, 87(2), 125-136.
• Barth R., Hemming, J., & van Henten, E. J. (2016). Design of an eye-in-hand sensing and servo control framework for harvesting robotics in dense vegetation. Biosystems Engineering, 146, 71-84.
• Basi S., Hunsche, M., Damerow, L., Lammers, P. S., & Noga, G. (2012). Evaluation of a pneumatic drop-on-demand generator for application of agrochemical solutions. Crop protection, 40, 121-125.
• Bawden O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., Lehnert C., & Perez, T. (2017). Robot for weed species plant‐specific management. Journal of Field Robotics, 34(6), 1179-1199.
• Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111.
• Binch A., & Fox, C. W. (2017). Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland. Computers and Electronics in Agriculture, 140, 123-138.
• Blasco J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). AE—Automation and emerging technologies: Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149-157.
• Bochtis D. D., Sørensen, C. G., & Busato, P. (2014). Advances in agricultural machinery management: A review. Biosystems engineering, 126, 69-81.
• Bontsema J., Van Asselt, C. J., Lempens, P. W. J., & Van Straten, G. (1998). Intra-row weed control: a mechatronics approach. IFAC Proceedings Volumes, 31(12), 93-97.
• Buddha K., Nelson, H. J., Zermas, D., & Papanikolopoulos, N. (2019). Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture. In 2019 27th Mediterranean Conference on Control and Automation (MED) (pp. 280-285). IEEE.
• Chebrolu N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W., & Stachniss, C. (2017). Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. The International Journal of Robotics Research, 36(10), 1045-1052.
• Çolak E. Ş., Yüksel, E., Canhilal, R. (2019). Yabancı otların kontrolünde biyolojik mücadele. Erciyes Tarım ve Hayvan Bilimleri Dergisi, 2(3), 23-29.
• F. Poulsen Engineering. (2017). Robovator. URL: http://www. visionweeding.com/robovator mechanical/ (accessed 10.04.19).
• Garford Corp, Robocrop Guided Hoes September (2014). http://www.garford.com
• Gerhards R., & Oebel, H. (2006). Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying. Weed research, 46(3), 185-193.
• Grira N., Crucianu, M., & Boujemaa, N. (2004). Unsupervised and semi-supervised clustering: a brief survey. A review of machine learning techniques for processing multimedia content, 1, 9-16.
• Grimstad L., & From, P. J. (2017). The Thorvald II agricultural robotic system. Robotics, 6(4), 24.
• Guijarro M., Pajares, G., Riomoros, I., Herrera, P. J., Burgos-Artizzu, X. P., & Ribeiro, A. (2011). Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 75(1), 75-83.
• Hamner B., Bergerman, M., & Singh, S. (2011). Autonomous orchard vehicles for specialy crop production. sl, ASABE Paper No. 11-071. St. Joseph, Mich.: ASABE.
• Hague Technology, T. (2017). Tillett and hague technology. http:// www.thtechnology.co.uk/.
• Harrell R. C., Slaughter, D. C., & Adsit, P. D. (1988). Robotics in agriculture. Dorf, RC (Ed.-in-Chief), International Encyclopedia of Robotics Applications and Automation. John Wiley & Sons, Inc., New York, 1378-1387.
• Heap I. (2014). Herbicide resistant weeds. In Integrated pest management (pp. 281-301). Springer, Dordrecht.
• Hillocks R. J. (2012). Farming with fewer pesticides: EU pesticide review and resulting challenges for UK agriculture. Crop Protection, 31(1), 85-93.
• Hiremath S. A., Van Der Heijden, G. W., Van Evert, F. K., Stein, A., & Ter Braak, C. J. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50.
• Hu J., Yan, X., Ma, J., Qi, C., Francis, K., & Mao, H. (2014). Dimensional synthesis and kinematics simulation of a high-speed plug seedling transplanting robot. Computers and electronics in agriculture, 107, 64-72.
• Isik D., Mennan, H., Cam, M., Tursun, N., Arslan, M. (2016). Allelopathic potential of some essential oil bearing plant extracts on Common Lambsquarters (Chenopodium album L.). Revista De Chimie. (Bucharest), 67(3), 455-459.
• Issues E. (2009). Food production must double by 2050 to meet demand from world's growing population, innovative strategies needed to combat hunger. New York, NY: Experts Tell Second Committee Press Release: United Nations.
• Kazmi W., Garcia-Ruiz, F., Nielsen, J., Rasmussen, J., & Andersen, H. J. (2015). Exploiting affine invariant regions and leaf edge shapes for weed detection. Computers and Electronics in Agriculture, 118, 290-299.
• Komi P. J., Jackson, M. R., & Parkin, R. M. (2007, June). Plant classification combining colour and spectral cameras for weed control purposes. In 2007 IEEE International Symposium on Industrial Electronics (pp. 2039-2042). IEEE.
• Kounalakis T., Triantafyllidis, G. A., & Nalpantidis, L. (2016). Weed recognition framework for robotic precision farming. In 2016 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 466-471). IEEE.
• Kounalakis T., Triantafyllidis, G.A., &Nalpantidis, L., (2018). A Robotic System Employing Deep Learning for Visual Recognition and Detection of Weeds in Grasslands. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE.
• Kounalakis T., Triantafyllidis, G. A., & Nalpantidis, L. (2019). Deep learning-based visual recognition of rumex for robotic precision farming. Computers and Electronics in Agriculture, 165, 104973.
• Kushwaha H. L., Sinha, J., Khura, T., Kushwaha, D. K., Ekka, U., Purushottam, M., & Singh, N. (2016, January). Status and scope of robotics in agriculture. In International Conference on Emerging Technologies in Agricultural and Food Engineering (Vol. 12, p. 163).
• Lee W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95-113.
• Lottes P., Hoeferlin, M., Sander, S., Müter, M., Schulze, P., & Stachniss, L. C. (2016). An effective classification system for separating sugar beets and weeds for precision farming applications. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5157-5163). IEEE.
• Lottes P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. (2017). UAV-based crop and weed classification for smart farming. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3024-3031). IEEE.
• Lund I., Søgaard, H. T., & Graglia, E. (2006). Micro-spraying with one drop per weed plant. In Third Danish Plant Production Congress, Denmark, 10-11 January, 2006 (pp. 451-452). Danish Institute of Agricultural Sciences.
• Mao H., Han, L., Hu, J., & Kumi, F. (2014). Development of a pincette-type pick-up device for automatic transplanting of greenhouse seedlings. Applied engineering in agriculture, 30(4), 547-556.
• McAllister W., Osipychev, D., Chowdhary, G., & Davis, A. (2018, October). Multi-agent planning for coordinated robotic weed killing. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7955-7960). IEEE.
• McCool C., Beattie, J., Firn, J., Lehnert, C., Kulk, J., Bawden, O., ... & Perez, T. (2018). Efficacy of mechanical weeding tools: A study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 3(2), 1184-1190.
• Mennan H., Ngouajio, M., Sahín, M., Isik, D. (2011). Allelopathic potentials of rice (Oryza sativa L.) cultivars leaves, straw and hull extracts on seed germination of barnyardgrass (Echinochloa crus-galli L.). Allelopathy Journal, 28(2).
• Mennan H., Ngouajio, M., Sahin, M., Isik, D., Kaya Altop, E. (2012). Quantification of momilactone B in rice hulls and the phytotoxic potential of rice extracts on the seed germination of Alisma plantago‐aquatica. Weed biology and management, 12(1), 29-39.
• Michaels A., Haug, S., & Albert, A. (2015). Vision-based high-speed manipulation for robotic ultra-precise weed control. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5498-5505). IEEE.
• Mohler C. L., Frisch, J. C., & Mt, J. (1997). Evaluation of mechanical weed management programs for corn (Zea mays). Weed Technology, 123-131.
• Nieuwenhuizen A. T. (2009). Automated detection and control of volunteer potato plants.
• Nishiwaki K., Amaha, K., Otani, R. (2004). Development of nozzle positioning system for precision sprayer. In Automation Technology for Off-Road Equipment Proceedings of the 2004 Conference (p. 74). American Society of Agricultural and Biological Engineers.
• Nof S. Y. (Ed.). (2009). Springer handbook of automation. Springer Science & Business Media.
• Ozer Z., Türkiye I. Herboloji Kongresi Bildirileri Adana, 1993, p.1.
• Pedersen S. M., Fountas, S., Have, H., & Blackmore, B. S. (2006). Agricultural robots—system analysis and economic feasibility. Precision agriculture, 7(4), 295-308.
• Peña J. M., Torres-Sánchez, J., Serrano-Pérez, A., De Castro, A. I., & López-Granados, F. (2015). Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 15(3), 5609-5626.
• Pérez-Ortiz M., Peña, J. M., Gutiérrez, P. A., Torres-Sánchez, J., Hervás-Martínez, C., & López-Granados, F. (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 37, 533-544.
• Pérez-Ortiz M., Peña, J. M., Gutiérrez, P. A., Torres-Sánchez, J., Hervás-Martínez, C., & López-Granados, F. (2016). Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Systems with Applications, 47, 85-94.
• Pérez-Ruíz M., Slaughter, D. C., Fathallah, F. A., Gliever, C. J., & Miller, B. J. (2014). Co-robotic intra-row weed control system. Biosystems engineering, 126, 45-55.
• Philipp I., & Rath, T. (2002). Improving plant discrimination in image processing by use of different colour space transformations. Computers and electronics in agriculture, 35(1), 1-15.
• Raja R., Nguyen, T. T., Slaughter, D. C., & Fennimore, S. A. (2020). Real-time weed-crop classification and localisation technique for robotic weed control in lettuce. biosystems engineering, 192, 257-274.
• Raja R., Slaughter, D. C., Fennimore, S. A., Nguyen, T. T., Vuong, V. L., Sinha, N., ... & Siemens, M. C. (2019). Crop signalling: A novel crop recognition technique for robotic weed control. biosystems engineering, 187, 278-291.
• Ren G., Lin, T., Ying, Y., Chowdhary, G., & Ting, K. C. (2020). Agricultural robotics research applicable to poultry production: A review. Computers and Electronics in Agriculture, 169, 105216.
• Rouveure R., Faure, P., & Monod, M. O. (2016). PELICAN: Panoramic millimeter-wave radar for perception in mobile robotics applications, Part 1: Principles of FMCW radar and of 2D image construction. Robotics and Autonomous Systems, 81, 1-16.
• Schor N., Bechar, A., Ignat, T., Dombrovsky, A., Elad, Y., & Berman, S. (2016). Robotic disease detection in greenhouses: combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robotics and Automation Letters, 1(1), 354-360.
• Shaner D. L. (2014). Lessons learned from the history of herbicide resistance. Weed Science, 62(2), 427-431.
• Singh S., Bergerman, M., Cannons, J., Grocholsky, B., Hamner, B., Holguin, G., ... & Li, G. (2010). Comprehensive automation for specialty crops: Year 1 results and lessons learned. Intelligent Service Robotics, 3(4), 245-262.
• Slaughter D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and electronics in agriculture, 61(1), 63-78.
• Slaughter D. C., Giles, D. K., & Tauzer, C. (1999). Precision offset spray system for roadway shoulder weed control. Journal of transportation engineering, 125(4), 364-371.
• Steketee. (2017). ICcultivator. https://www.steketee.com/en/ steketee-ic-weeder/.
• Stentz A., Dima, C., Wellington, C., Herman, H., & Stager, D. (2002). A system for semi-autonomous tractor operations. Autonomous Robots, 13(1), 87-104.
• Sujaritha M., Annadurai, S., Satheeshkumar, J., Sharan, S. K., & Mahesh, L. (2017). Weed detecting robot in sugarcane fields using fuzzy real time classifier. Computers and electronics in agriculture, 134, 160-171.
• Sujaritha M., Lakshminarasimhan, Mahesh, Jude Fernandez, Colin, Chandran, Mahesh. (2016). Greenbot: A solar autonomous robot to uproot weeds in a grape field. International journal of computer science and engineering communications, 4(2), 1351-1358.
• Su W. H., Slaughter, D. C., & Fennimore, S. A. (2020). Non-destructive evaluation of photostability of crop signaling compounds and dose effects on celery vigor for precision plant identification using computer vision. Computers and Electronics in Agriculture, 168, 105155.
• Sünderhauf N., McCool, C., Upcroft, B., & Perez, T. (2014). Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction. In CLEF (Working Notes) (pp. 756-762).
• Søgaard H.T., & Lund, I. (2005). Investigation of the accuracy of a machine vision based robotic micro-spray system. In Investigation of the accuracy of a machine vision based robotic micro-spray system (pp. 613-619).
• 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), 171-178.
• Tillett N. D., Hague, T., & Miles, S. J. (2002). Inter-row vision guidance for mechanical weed control in sugar beet. Computers and electronics in agriculture, 33(3), 163-177.
• Utstumo T., Urdal, F., Brevik, A., Dørum, J., Netland, J., Overskeid, Ø., Berge, T. W., Gravdahl, J. T. (2018). Robotic in-row weed control in vegetables. Computers and electronics in agriculture, 154, 36-45.
• Xue J., Zhang, L., & Grift, T. E. (2012). Variable field-of-view machine vision based row guidance of an agricultural robot. Computers and Electronics in Agriculture, 84, 85-91.
• Van Der Weide R., Bleeker, P. O., Achten, V. T. J. M., Lotz, L. A. P., Fogelberg, F., & Melander, B. (2008). Innovation in mechanical weed control in crop rows. Weed research, 48(3), 215-224.
• Van Evert F. K., Polder, G., Van Der Heijden, G. W. A. M., Kempenaar, C., & Lotz, L. A. P. (2009). Real‐time vision‐based detection of Rumex obtusifolius in grassland. Weed Research, 49(2), 164-174.
• Van Evert F. K., Samsom, J., Polder, G., Vijn, M., Dooren, H. J. V., Lamaker, A., ... & Lotz, L. A. (2011). A robot to detect and control broad‐leaved dock (Rumex obtusifolius L.) in grassland. Journal of Field Robotics, 28(2), 264-277.
• Van Henten E. J., Van Tuijl, B. V., Hemming, J., Kornet, J. G., Bontsema, J., & Van Os, E. A. (2003). Field test of an autonomous cucumber picking robot. Biosystems engineering, 86(3), 305-313.