Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Review Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Smart mechanization of tapioca planting: Integrating AI and advanced technologies

DOI
https://doi.org/10.14719/pst.8234
Submitted
13 March 2025
Published
29-05-2025

Abstract

Conventional farming guidelines lack detail, providing crop suggestions without location-based advice. This makes it challenging for farmers to make informed decisions about where to plant crops, affecting their productivity and profitability. The reason for tackling this issue lies in the potential of cassava, a crop that can withstand drought conditions to thrive if the right areas are identified accurately. This evaluation has a look at the way in which advanced technologies as well as artificial intelligence (AI) can be used to revolutionize mechanized cassava planting, with the objective of increasing productivity and sustainability. Cassava, a nutrition base for over 500 million people worldwide, is sourced from India, especially in the Tamil Nadu and Kerala regions. However, its existence is subject to certain problems that are linked to the conventional method of planting, like inconsistency in planting depths, wrong sweating of the stem and clogging of the channel, which all in turn serve to lower yields and increase labor costs. The former factors caused by mechanical devices like two-ploughed and three-ploughed planters have already been close to the economies of scale by achieving better planting efficiencies and consistency, but they are still presenting some difficulties. Some of the latest advances, such as tractor-operated single-row stake cutter-planters and rotary dibble-type planters, provide alternatives that are not only cheaper but also have the highest degree of accuracy when it comes to planters. Furthermore, the introduction of IoT (Internet of Things) and machine learning and the use of big data analytics in farming are the new directions for precision agriculture. This review emphasizes that the research that is continually being carried out, the unbeatable innovation or technological development and the farmer education that is expected to be high enough are crucial for the full realization of smart in cassava cultivation. In this way, we will be closer to a more sustainable type of agriculture and improved food security. By embracing these innovations, agriculture can be modernized, empowering farmers to overcome obstacles and achieve increased productivity. The data and findings highlighted in the review demonstrate the effectiveness of mechanization in revolutionizing practices and bolstering global food security.

References

  1. 1. Shankar RG, Rajesh R, Selvam S. A study on resource use efficiency and economic returns for Tapioca in Namakkal district of Tamil Nadu, India. The Pharma Innovation Journal [Internet]. 2021;(9):400–4. http://www.thepharmajournal.com
  2. 2. Jegan S, Raj RA, Elakkiya M, Madan P. Automation in cassava plantation. International Journal of Scientific & Technology Research [Internet]. 2020;9(03):2035–7. www.ijstr.org
  3. 3. Senthilkumar T, Ganeshamoorthy J, Annamalai SJ, Mohanraj E. Development and evaluation of tractor operated cassava stake cutter planter. 2023;13-8.
  4. 4. FAO. Save and grow: Cassava [Internet]. [cited 2025 Feb 3]. https://www.fao.org/faostat/en
  5. 5. Marechera G, Muinga G. Value chain approaches to mechanization in cassava cultivation and harvesting in Africa. 2017;375–98.
  6. 6. Ogundare SK. Effect of depth of planting, methods of planting and animal residues application on the growth and yield performance of cassava in Ejiba, Kogi state, Nigeria. 2016.
  7. 7. Ospina Patiño B, Fernando Cadavid LL, García M, Alcalde C. Mechanized systems for planting and harvesting cassava (Manihot esculenta Crantz). 2012.
  8. 8. ICAR-CIAE Modernizing agriculture through engineering interventions [Internet]. 2020. www.ciae.icar.gov.in
  9. 9. He F, Deng G, Cui Z, Li L, Li G. Development of a rotary dibble-type cassava planter. Engenharia Agrícola. 2022;42(5).
  10. 10. Dinesh Pandi M, Asokan D, John Gunasekar J, Vallal Kannan S. Assessing suitability of fluted roller metering mechanism for cassava sets planter. Int J Curr Microbiol Appl Sci. 2019;8(05):1951–7.
  11. 11. Lungkapin J, Salokhe VM, Kalsirisilp R, Nakashima H. Development of a stem cutting unit for a cassava planter. 2007.
  12. 12. Benigno Engr JL. Design and development of a prototype implement-type cassava planting machine. International Journal of Scientific and Research Publications (IJSRP). 2021;11(4):165–70.
  13. 13. Dhananchezhiyan P, Sathish Kumar N, Sindhujaa J. Design and development of power operated portable cassava sett cutter for small farmers. An International Refereed, Peer Reviewed & Indexed Quarterly Journal in Science, Agriculture & Engineering. 2020.
  14. 14. Vikranth KP. An Implementation of IoT and data analytics in smart agricultural system – a systematic literature review. International Journal of Management, Technology and Social Sciences. 2021;41–70.
  15. 15. Yang L, Nasrat LS, Badawy ME, Wapet DEM, Ourapi MA, El-Messery TM, et al. A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor. PLoS One. 2024;19.
  16. 16. Zhou D, Fan Y, Deng G, He F, Wang M. A new design of sugarcane seed cutting systems based on machine vision. Comput Electron Agric. 2020;175:105611.
  17. 17. Ale MO, Manuwa SI, Olukunle. Effect of forward speed and drive wheel on the performance of a semi-automatic cassava planter [Internet]. Vol. 4, Original Research. 2023. www.achieversjournalofscience.org
  18. 18. Wang W, Li C, Wang K, Tang L, Ndiluau PF, Cao Y. Sugarcane stem node detection and localization for cutting using deep learning. Front Plant Sci. 2022;13.
  19. 19. Narmilan A, Puvanitha S. The effect of different planting methods on growth and yield of selected cassava (Manihot esculenta) cultivars. Agricultural Science Digest - A Research Journal. 2020.
  20. 20. K Mbugua J, Suksa-ngiam W. Predicting suitable areas for growing cassava using remote sensing and machine learning techniques: a study in Nakhon-Phanom Thailand. Issues in Informing Science and Information Technology. 2018;15:043–56.
  21. 21. Guojie LI. AI4R: The fifth scientific research paradigm [J]. Bulletin of Chinese Academy of Sciences. 2024;1.

Downloads

Download data is not yet available.