GNSS Esaslı Traktör Otomatik Dümenleme Sistemlerinin Ekim İşleminde Paralel Geçişlerde Sıra Arası Mesafe Düzgünlüğüne Etkisi

Year 2024, Volume: 21 Issue: 1, 46 - 63, 30.01.2024

Mustafa Topcueri , Muharrem Keskin , Yunus Emre Şekerli

https://doi.org/10.33462/jotaf.1231452

Cited By: 1

Abstract

Hassas tarım (HT), toprak işlemeden hasada kadar tarımsal faaliyetlerin verimliliğini ve kârlılığını artırmayı, doğal kaynakların ve çevrenin korunmasını ve böylelikle sürdürülebilirliği hedefleyen ileri teknolojileri içerir. Otomatik dümenleme (OD), dünyada ve Türkiye'de en yaygın kullanılan ve birçok fayda sağlayan HT teknolojisidir. OD; sırta toprak işleme ve ekim dâhil olmak üzere tarımsal işlemlerde verimli ve sürdürülebilir uygulamalara imkân sağlar. Makinalı hassas ekimde, her bitkiye eşit yaşam alanı sağlamak için bitki sıra aralarında yeterli boşluk gereklidir. Bu nedenle makinalı ekimde paralel yan yana geçişlerde bitki sıra arası mesafesi (BSAM) eşit olmalıdır. Ekimde eşit BSAM sağlamak için OD'nin faydalarına ilişkin yapılan araştırmalar oldukça sınırlıdır. Bu çalışma, üç farklı sinyal düzeltme kaynağı (RTK, CORS, SBAS) kullanan GNSS esaslı OD ile ekimde yan yana paralel geçişlerdeki PIRS sapmalarını karşılaştırmak amacıyla yapılmıştır. Çalışmada OD kullanılmayan (manuel yönlendirmeli) tarlalardaki BSAM değerleri de karşılaştırma amacıyla incelenmiştir. Veriler, Çukurova bölgesinde, sıra arası mesafe değerleri 70-75 cm olan 24 çiftçi tarlasından (pamuk ve mısır) elde edilmiştir. Paralel yan yana geçişlerdeki BSAM değerleri manuel olarak ölçülmüş ve ayarlanan değerden olan sapmalar hata kareler ortalamasının karekökü (RMSE) değeri ile analiz edilmiştir. Manuel dümenlemeli ekimde ortalama BSAM sapmaları (7.4 cm), OD ile yapılan sırta toprak işleme ve/veya ekime göre daha yüksek bulunmuştur (CORS: 5.0 cm, SBAS: 5.9 cm, RTK: 6.7 cm) (p<0.05). Özetle, OD teknolojisinin yan yana paralel geçişlerde BSAM değişimlerini azaltmada fayda sağladığı ancak fayda düzeyinin çiftçiden çiftçiye değiştiği belirlenmiştir. Bu nedenle, ileri düzeyde faydalar elde edebilmek için OD sistemleri uygun ayarlarla dikkatli bir şekilde kullanmalıdır.

Keywords

Hassas tarım, otomatik dümenleme, GNSS, ekim, bitki sıra arası mesafe

Project Number

Project no: 18.M.079

Efficiency of GNSS-based Tractor Auto Steering for the Uniformity of Pass-to-Pass Plant Inter-Row Spacing

Abstract

Precision agriculture (PA) includes advanced technologies to increase efficiency and profitability of agricultural operations from tillage to harvest and offers sustainability of the natural resources and the environment. Automatic steering (AS) is the mostly-used PA technology in the world and in Türkiye providing many benefits. It has potential for efficient and sustainable agronomic practices including soil ridge tillage and sowing. Adequate spacing is needed to provide equal living area for each plant in sowing. Thus, in mechanized planting, pass-to-pass plant inter-row spacing (PIRS) should be equal in parallel passes. Research on the benefits of the AS for providing uniform PIRS in sowing is very limited. This work aimed to appraise the pass-to-pass PIRS deviations in planting with GNSS-based AS with three signal correction sources (RTK, CORS, SBAS) and without AS (manual steering) for comparison. The data were obtained from 24 farmer fields (cotton and corn) with PIRS set values of 70-75 cm located in the Cukurova region of Türkiye. Pass-to-pass PIRS values were manually measured and the deviations from the set value were analyzed in terms of root mean square error (RMSE). The mean PIRS variations in sowing by manual steering (7.4 cm) were found as significantly higher than the AS based soil ridge tillage and / or sowing (CORS: 5.0 cm, SBAS: 5.9 cm, RTK: 6.7 cm) (p<0.05). In sum, it was found that the AS technology offers benefits in lowering the pass-to pass PIRS variations but the level of benefit changes from farmer to farmer; hence, the AS should be used cautiously with proper settings for greater benefits.

Keywords

Precision agriculture, Auto steering, GNSS, Sowing, Plant inter-row spacing

Supporting Institution

Scientific Research Project Office (BAP) of the Hatay Mustafa Kemal University

Project Number

Project no: 18.M.079

Thanks

The authors thank the farmers who took part in this study by allowing data collection from their fields. They also thank Dr. Sait M. Say for his assistance in field data collection.

References

• Akdemir, B. (2016). Evaluation of Precision Farming Research and Applications in Turkey. VII International Scientific Agriculture Symposium, Agrosym 2016, pp.1498-1504., 6-9 October, Jahorina, Bosnia and Herzegovina.
• Altinkaradag, A., Akdemir, B., Kesici, E. and Urusan, A. Y. (2017). Development of Automatic Steering System for Tractors. 13th International Congress on Mechanization and Energy in Agriculture & Workshop on Precision Agriculture, Page 60, 13-15 September, Izmir, Turkiye.
• Anastasiou, E., Fountas, S., Voulgaraki, M., Psiroukis, V., Koutsiaras, M., Kriezi, O., Lazarou, E., Vatsanidou, A., Fu, L., Bartolo, F.D., Barreiro-Hurle, J. and Gómez-Barbero, M. (2023). Precision farming technologies for crop protection: A meta-analysis. Smart Agricultural Technology, 5: 100323.
• ASABE. (2015). Tractors and Machinery for Agriculture and Forestry: Test Procedures for Positioning and Guidance Systems in agriculture. Part 2: Testing of Satellite-Based Auto-Guidance Systems During Straight and Level Travel. ASABE/ISO 12188-2: 2012.
• Ashworth, A. J., Lindsay, K. R., Popp, M. P. and Owens, P. R. (2018). Economic and environmental impact assessment of tractor guidance technology. Agricultural & Environmental Letters. 3(1): 180038.
• Baillie, C. P., Lobsey, C. R., Diogenes, L. A., McCarthy, C. L. and Thomasson, J. A. (2018). A Review of the State of the Art in Agricultural Automation. Part III: Agricultural Machinery Navigation Systems. ASABE Annual International Meeting, 29-31 July, Detroit, MI, USA.
• Baio, F. H. R. and Moratelli, R. F. (2011). Auto guidance accuracy evaluation and contrast of the operational field capacity on the mechanized plantation system of sugar cane. Engenharia Agrícola, 31: 367-375.
• Batte, M. T. and Ehsani, M. R. (2006). The economics of precision guidance with auto-boom control for farmer-owned agricultural sprayers. Computers and Electronics in Agriculture, 53(1): 28-44.
• Bayar, G., Bergerman, M., Koku, A. B. and Konukseven, E. I. (2015). Localization and control of an autonomous orchard vehicle. Computers and Electronics in Agriculture, 115: 118–128.
• Blessing, C., Nhamo, M. and Rangarirai, M. (2020). The impact of plant density and spatial arrangement on light interception on cotton crop and seed cotton yield: an overview. Journal of Cotton Research, 3(1): 1-6.
• Bora, G. C., Nowatzki, J. F. and Roberts, D.C. (2012). Energy savings by adopting precision agriculture in rural USA. Energy, Sustainability and Society, 2: 1-5.
• Burgers, T. A. and Vanderwerf, K. J. (2022). Vision and radar steering reduces agricultural sprayer operator stress without compromising steering performance. Journal of Agricultural Safety and Health, 28(3): 163-179.
• Civelek, C. (2022). Development of an IoT-based (LoRaWAN) tractor tracking system. Journal of Agricultural Sciences, 28(3): 438-448.
• D'Antonio, P., Mehmeti, A., Toscano, F. and Fiorentino, C. (2023). Operating performance of manual, semi-automatic, and automatic tractor guidance systems for precision farming. Research in Agricultural Engineering, 69(4): 179-188.
• DeLay, N. D., Thompson, N. M. and Mintert, J. R. (2022). Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73: 195–219.
• Easterly, D., Adamchuk, V. I., Hoy, R. M. and Kocher, M. F. (2010). Testing of RTK-level Satellite-based Tractor Auto-guidance using a VISUAL Sensor System. 2nd International Conference on Machine Control & Guidance. 9-11 March, University of Bonn, Germany.
• Erickson, B. and Widmar, D. A. (2015). Precision Agricultural Services Dealership Survey Results. Purdue University. Indiana, USA. 37pp.
• Garcia-Santillan, I. D., Montalvo, M., Guerrero, J. M. and Pajares, G. (2017). Automatic detection of curved and straight crop rows from images in maize fields. Biosystems Engineering, 156: 61-79.
• Gisgeography (2018). How to Calculate Root Mean Square Error (RMSE) in Excel. https://gisgeography.com/root-mean-square-error-rmse-gis/ (Accessed date: 30.12.2018)
• Grisso, R., Alley, M. and Groover, G. (2009). Precision Farming Tools: GPS Navigation. Virginia Cooperative Extension. Publication No 442-501.
• Hudson, G., Shofner, R., Wardlow, G. and Johnson, D. (2007). Evaluation of three tractor-guidance methods for parallel swathing at two field speeds. Discovery, 8: 61-66.
• Jotautiene, E., Juostas, A. and Venslauskas, K. (2021). Evaluation of harvesting driving modes from environmental point of view. Biology and Life Sciences Forum, 3(1): 44, 1-7.
• Juostas, A. and Jotautiene, E. (2021). Evaluation of combine harvester parameters using manual and auto-steering modes. 20th International Scientific Conference on Engineering for Rural Development, 26-28 May, Jelgava, Latvia.
• Keskin, M. and Sekerli, Y. E. (2016). Awareness and adoption of precision agriculture in the Cukurova region of Turkey. Agronomy Research, 14(4): 1307-1320.
• Keskin, M. and Sekerli, Y. E. (2022). Precise, autonomous and smart systems in weed control in agriculture AKITEK 4.0 Dergisi, 1(1): 54-63 (In Turkish).
• Keskin, M., Sekerli, Y. E., Say, S. M. and Topcueri, M. (2018). Farmers’ experiences with GNSS-based tractor auto guidance in Adana province of Turkey. Journal of Agricultural Faculty of Gaziosmanpasa University, 35(2): 172-181.
• Kharel, T. P., Ashworth, A. J., Michael, A. S., Popp, P. and Owens, P. R. (2020). Tractor guidance improves production efficiency by reducing overlaps and gaps. Agriculture Environment Letters, 5: e20012.
• Leonard, E. (2014). Precision Agriculture Down Under. www.precisionag.com/guidance/precision-ag-down-under (Accessed date: 07.01.2023)
• Li, M., Imou, K., Wakabayashi, K. and Yokoyama, S. (2009). Review of research on agricultural vehicle autonomous guidance. Journal of Agricultural & Biological Engineering, 2(3): 1-26.
• Masi, M., Di Pasquale, J., Vecchio, Y., and Capitanio, F. (2023) Precision farming: Barriers of variable rate technology adoption in Italy. Land, 12(5), 1084, 2-18, https://doi.org/10.3390/land12051084
• McFadden, J., Njuki, E. and Griffin, T. (2023). Precision Agriculture in The Digital Era: Recent Adoption on U.S. farms. USDA Economic Research Service. Economic Information Bulletin. Number 248. 56pp.
• Mizik, T. (2022). How can precision farming work on a small scale? A systematic literature review. Precision Agriculture, 24: 384–406.
• Mousazadeh, H. (2013). A technical review on navigation systems of agricultural autonomous off-road vehicles. Journal of Terramechanics, 50: 211–232.
• Oksanen, T. and Backman, J. (2016). Implement guidance model for ISO 11783 standard. IFAC-Papers On Line, 49(16): 33-38.
• Ozguven, M. M. and Turker, U. (2010). The production economics of precision farming and its possible application for grain corn in Turkey. Journal of Tekirdag Agricultural Faculty, 7(1): 55-70.
• Reichhardt (2012). Auto Guidance System Brochure. Reichhardt, Sabin, MN 56580, USA. www.reichhardt.com (Accessed date: 30.12.2018)
• Santos, A. F., Correa, L. N., Girio, L. A. S., Paixao, C. S. S. and da Silva, R. P. (2018). Position errors in sowing in curved and rectilinear routes using autopilot. Engenharia Agricola, 38: 568-576.
• Santos, A. F., Silva, R. P., Tavares, T. O., Ormond, A. T. S., Rosalen, D. L. and Assis, L. C. (2017). Parallelism error in peanut sowing operation with auto-steer guidance. Revista Brasileira de Engenharia Agrícola e Ambiental, 21(10): 731-736.
• Say, S. M., Keskin, M., Sehri, M. and Sekerli, Y. E. (2017). Adoption of Precision Agriculture Technologies in Developed and Developing Countries. International Science and Tech. Conference, pp.41-49, 17-19 July, Berlin, Germany.
• Scarfone, A., Picchio, R., del Giudice, A., Latterini, F., Mattei, P., Santangelo, E. and Assirelli, A. (2021). Semi-automatic guidance vs. manual guidance in agriculture: A comparison of work performance in wheat sowing. Electronics, 10(7): 1-13.
• Silva, C. B., Moraes, M. A. F. and Molin, J. P. (2011). Adoption and use of precision agriculture technologies in the sugarcane industry of Sao Paulo state, Brazil. Precision Agriculture, 12: 67–81.
• Tekin, A. B. (2011). Information and communication technology: An assessment of Turkish agriculture. Outlook on Agriculture, 40(2): 147-156.
• Tilley, M. S., Jordan, D. L., Heiniger, R. W., Vann, R., Crozier, C. R. and Gatiboni, L. (2021). A survey of twin-row cropping systems in North Carolina. Crop Forage and Turfgrass Management, 7: e20099.
• Topcueri, M., Keskin, M. (2019). Effectiveness of GNSS-based tractor auto steering systems in crop spraying. Mustafa Kemal University Journal of Agricultural Sciences, 24: 78-90.
• Unal, I. and Topakci, M. (2012). Navigation Methodology and the Different Navigation Systems for the Agricultural Applications. 27th National Agricultural Mechanization Congress. 5-7 September, Samsun, Turkey.
• Verma, L. (2015). China pursues precision agriculture on a grand scale. Resource Magazine. ASABE, July/August 2015, 22: 18–19.
• Voltarelli, M. A., Silva, R. P., Rosalen, D. L., Zerbato, C. and Cassia, M. T. (2013). Quality of performance of the operation of sugarcane mechanized planting in day and night shifts. Australian Journal of Crop Science, 7: 1396-1406.
• Vrchota, J., Pech, M. and Svepesova, I. (2022). Precision agriculture technologies for crop and livestock production in the Czech Republic. Agriculture, 12(8): 1080.
• Vrochidou, E., Oustadakis, D., Kefalas, A. and Papakostas, G. A. (2022). Computer vision in self-steering tractors. Machines, 10(2): 129, 2-22.
• Yaghoubi, M. and Niknami, M. (2022). Challenges of precision agriculture application in pistachio orchards: factor analysis from Iranian agricultural experts’ perspective. Journal of Tekirdag Agricultural Faculty, 19(3): 473-482.
• Yun, C., Kim, H. J., Jeon, C. W., Gang, M., Lee, W.S. and Han, J. G. (2021). Stereovision-based ridge-furrow detection and tracking for auto-guided cultivator. Computers and Electronics in Agriculture, 191: 106490.
• Zerbato, C., Furlani, C. E. A., de Oliveira, M. F., Voltarelli, M. A., Tavares, T. O. and Carneiro, F. M. (2019). Quality of mechanical peanut sowing and digging using autopilot. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(8): 630-637.