Skip to content
Research Article Open access CC BY 4.0

Unmanned Aerial Vehicle Direct Seeding Versus Ground Seeding Mechanization Services in Smallholder Farming Systems of North West IGP on Energy Use Efficiency and Quality of Rice Culture: A Review

P. K. Singh, Arun Kumar, R. K. Naresh, Raghvendra Shah Sisodi, Rojalin Hota, Nibedan Halder, Dhritiman Das, P. Viviana, Himanshu Tiwari, Suneel Kumar

International Journal of Environment and Climate Change · pp. 2105–2121 · Published 26 Jul 2023

10.9734/ijecc/2023/v13i92444

Abstract

Various sectors use AI technologies to increase output and productivity. AI in agriculture also enables farmers to boost productivity while minimizing harmful environmental effects. AI is transforming the food-processing industry, where agricultural emissions have dropped by 20%. Mechanization is once again one of the top development policy targets for changing the smallholder agricultural system in the north west IGP. To support public investment or company development programs, however, previous and ongoing efforts frequently suffer from a lack of scientific data on end-user effective demand for various sorts of mechanical improvements. Rice production involves intricate agronomic procedures. Seeding, fertilizing, and pesticide application are labour- and time-intensive tasks that have low automation efficiency. Currently, a lot of research focuses on the single UAV operation on rice, but there aren't many applications that cover the entire process of sowing, fertilizing, and applying pesticides. Based on the intelligent operating platform, a mUAV was created to oversee the planting of rice. This aircraft accomplished three tasks on the same flight platform: seeding, fertilizer application, and pesticide application. Machine design was carried out using simulations of CFD. The cultivation patterns of mechanical rice direct seeder, mechanical rice transplanter, and mUAV seeding were compared to perform a comparative evaluation of the entire process. With improved rice automation, fewer labour inputs, and lower costs, it is intended that this evaluation will offer new machinery for rice farming patterns in various conditions.

Mechanisation unmanned aerial vehicle direct seeded rice

References (16)

  1. 1 Machine Learning in Agriculture: A Review [DOI]
  2. 2 A compilation of UAV applications for precision agriculture [DOI]
  3. 3 Rice: Importance for Global Nutrition [DOI]
  4. 4 Rice direct seeding: Experiences, challenges and opportunities [DOI]
  5. 5 Gene expression profile analysis of the rat macrophages infected with Toxoplasma gondii [DOI]
  6. 6 A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture [DOI]
  7. 7 Current status and future trends of precision agricultural aviation technologies [DOI]
  8. 8 Simulation and parameter optimisation of a centrifugal rice seeding spreader for a UAV [DOI]
  9. 9 Rice production systems and grain quality [DOI]
  10. 10 Review of precision rice hill-drop drilling technology and machine for paddy [DOI]
  11. 11 The Prospect of Artificial Intelligence (AI) in Precision Agriculture for Farming Systems Productivity in Sub-Tropical India: A Review [DOI]
  12. 12 Research Status and Development Discussion on High-Yielding Agronomy of Mechanized Planting Rice in China
  13. 13 Global Rice Production, Consumption and Trade: Trends and Future Directions
  14. 14 Research status and development trend of UAV broadcast sowing technology in China [DOI]
  15. 15 Status and Prospect on Rice Direct Seeding Technology of Farmers [DOI]
  16. 16 Current Journal of Applied Science and Technology [DOI]

Cited by 6

DESIGN AND EXPERIMENT OF SWING TYPE SYNCHRONOUS MULCHING FOR RICE DIRECT SEEDER

Jiajia YU, Zhongyan TAO, Liping XIAO · INMATEH Agricultural Engineering · 2024

Designing an Automated Seeding Drone System for Soil-Condition-Based Multi-Crop Farming

Mareme Moshito Petja, Makhulu Relebogile Langa, Michael Nthabiseng Moeti · 2025 5th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · 2025

Showing 3 of 6 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

47

Page views

0

PDF downloads

8

Outbound clicks

6

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.