Abstract
Sustainable development goals (SDGs) aim to increase productivity and utilization of resources in the agricultural sector. However, the agricultural sector is currently confronted with the effects of climate change and workforce shortages. The population has risen such that food security has become vital. Furthermore, the global agriculture industry is struggling with issues such as labor and farmer scarcity as well as rising labor costs. To bring a solution to this current trend, the problems of agricultural labor forces, automation, sensor advancements, artificial intelligence, and IoT that can support more young farmers are highly required. The innovations and implementation of advanced machinery can be stratified based on regional demand and population engagement in the agriculture sector both in developed and developing nations. There are six degrees of mechanization and automation are significant noted: Level 0 refers to no automation, Level 1 is assistance in automation, Level 2 outlines partial automation, Level 3 is conditional automation, Level 4 is high automation, and Level 5 is full automation considering the sensing system lateral and longitudinal control of machinery. Therefore, the purpose of this chapter is to discuss the application levels of mechanization to support labor shortages and increase productivity in developed and developing countries. This article solely describes the current trend in agricultural machinery adoption and the levels of mechanization that can be recommended for appropriateness globally. In addition to level selection, this article introduces sensors and transformation stages with low-cost automation, specifically shifting toward autonomous machinery development in the future spectrum.
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References
Ahamed, T., Takigawa, T., Noguchi, R., & Tian, L. (2014). Bioproduction engineering: A road map for sustainable agricultural practices. Nova Science Publishers. ISBN: 978-1-62618-122-9.
Antille, D. L., Gallar, L., Miller, P. C., & Godwin, R. J. (2015). An investigation into the fertilizerparticle dynamics off-the-disc. Applied Engineering in Agriculture, 31(1), 49–60. https://doi.org/10.13031/aea.31.10729
Aryal, J. P., Thapa, G., & Simtowe, F. (2021). Mechanisation of small-scale farms in South Asia: Empirical evidence derived from farm households survey. Technology in Society, 65, 101591. https://doi.org/10.1016/j.techsoc.2021.101591
Autospraysystems. (n.d.). R150 robot. Retrieved from https://autospraysystems.com/robot-1
Baillie, C., et al. (2017). Developments in autonomous tractors. Retrieved from https://grdc.com.au/resources-and-publications /grdc-update-papers/tab-content/grdc-update-papers/2017/07/developments-in-autonomous-tractors
Baillie, C., et al. (2018). A review of the state of the art in agricultural automation. A review of the state of the art in agricultural automation. Part III: Agricultural machinery navigation systems. In 2018 ASABE annual international meeting (p. 1). American society of agricultural and biological engineers.
Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014
Burks, T., Villegas, F., Hannan, M., Flood, S., Sivaraman, B., Subramanian, V., & Sikes, J. (2005). Engineering and horticultural aspects of robotic fruit harvesting: Opportunities and constraints. HortTechnology, 15(1), 79–87. https://doi.org/10.21273/HORTTECH.15.1.0079
Deere, J. (n.d.). Future of farming. Retrieved from John Deere: https://www.deere.co.uk/en/agriculture/future-of-farming/
EC (2017). EU agricultural outlook for the agricultural markets and income 2017–2030. European commission. Retrieved from https://agriculture.ec.europa.eu/data-and-analysis/markets/outlook/medium- term_en
FAO. (2017). Retrieved from https://www.fao.org/sustainable-agricultural-mechanization/overview/why-mechanization-isimportant/en/
Fendt Xaver. (2020). Latest generation of seed sowing robots: The Fendt Saver comes of age. Retrieved from https://www.fendt.com/int/2-fendt-xaver
Goense, D. (2005). The economics of autonomous vehicles in agriculture. In 2005 ASAE annual meeting (p. 1). American society of agricultural and biological engineers.
Harmanda, T. T., Hazim, M., Akbar, A. R., Wahjuni, S., & Priandana, K. (2019, July). Development of a seed-planter wheeled robot prototype. In IOP conference series: Earth and environmental science (Vol. 299, No. 1, p. 012055). IOP Publishing.
Hayami Y., & Ruttan, V. (1971). Induced innovation in agricultural development (Vol. Discussion Paper No.3). Minneapolis, Minnesota 55455. Retrieved from https://conservancy.umn.edu/bitstream/handle/11299/54243/1971-03.pdf;sequence=1
Oitzman, M. (2022, April 22). John Deere forms joint venture with GUSS Automation. Retrieved from The Robot Report: https://www.therobotreport.com/john-deere-forms-joint-venture-with-guss-automation/
Rinnan, A., Sigmond, M. E., Robertsen, A., & Gundersen, N. (2009). Qualification of a Hybrid GNSS and IMU Solution. Kongsberg Seatex AS, Trondheim, Norway, DP Conference Houston, p. 19. Retrieved from http://dynamic-positioning.com/proceedings/dp2009/sensors_rinnan.pdf
SAE. (2021). SAE levels of driving automationâ„¢ refined for clarity and international audience. Society of automatics engineers international. Retrieved from https://www.sae.org/blog/sae-j3016-update
Schueller, J. K. (2014). Engineering advancements. In automation:The future of weed control in cropping systems (pp. 35–49). Springer. https://doi.org/10.1007/978-94-007-7512-1_3
Sims, B., & Kienzle, J. (2017). Sustainable agricultural mechanization for smallholders: what is it and how can we implement it? Agriculture, 7(6), 50. https://doi.org/10.3390/agriculture7060050
Statistics Bureau of Japan. (2016). Japan Statistical Yearbook. Retrieved from https://www.stat.go.jp/english/data/nenkan/65nenkan/index.html
Tassell, L. V. (2022, July 6). Agricultural Economics - University of Nebraska–Lincoln. Retrieved from University of Nebraska–Lincoln. https://agecon.unl.edu/2022-07-06%20Cornhusker%20Econ%20-%20Van%20Tassell%20-%203.pdf
Villette, S., Piron, E., & Miclet, D. (2017). Hybrid centrifugal spreading model to study the fertiliser spatial distribution and its assessment using the transverse coefficient of variation. Computers and Electronics in Agriculture, 137, 115–129. https://doi.org/10.1016/j.compag.2017.03.023
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Minn, A., Ahamed, T. (2023). Low-Cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Small-Scale Farmers to Achieve SDGs. In: Ahamed, T. (eds) IoT and AI in Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-19-8113-5_15
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DOI: https://doi.org/10.1007/978-981-19-8113-5_15
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