Skip to content
Research Article Open access CC BY 4.0

Precision Agriculture Using Remote Sensing and GIS for Peanut Crop Production in Arid Land

M. M. El-Sharkawy, A. S. Sheta, M. S. Abd El-Wahed, S. M. Arafat, O. M. El Behiery

International Journal of Plant & Soil Science · pp. 1–9 · Published 5 Mar 2016

10.9734/IJPSS/2016/20539

Abstract

Precision agriculture is a farming management approach for a whole field with a potentiality to solve some of management problems based on observing and measuring field crops variability using more accurate and timely information of agricultural resources. Site-specific management for farming operations and data mining using good sampling design is an effective tool on precision agriculture while remote sensing facilities are perfect tools to assess the land cover, crop situation and status as well as their changes. This work aimed to identify management zones for Peanut crop using precision agriculture management practices. GIS, GPS, sensors and soil sampling are the major technological components which were used for that purpose. The results showed that using variable rate technology and management zones for Peanut crop production is greatly responsible for lowering cost of input and decreasing environmental impact using the least amount of chemicals necessary. Furthermore, soil suitability was successfully employed to simulate soil characteristics effect on canopy structure and final yield.  

Precision agriculture peanut remote sensing GIS and

References (20)

  1. 1 A soil-adjusted vegetation index (SAVI) [DOI]
  2. 2 The analytic hierarchy process : planning, priority setting, resource allocation
  3. 3 Optimization of soil-adjusted vegetation indices [DOI]
  4. 4 Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation
  5. 5 RASTER PROCEDURES FOR MULTI-CRITERIA/MULTI-OBJECTIVE DECISIONS
  6. 6 Comparison of broad-band and narrow-band red and near-infrared vegetation indices [DOI]
  7. 7 Correlation of Corn and Soybean Grain Yield with Topography and Soil Properties [DOI]
  8. 8 Remote sensing applications for precision agriculture: A learning community approach [DOI]
  9. 9 Application of MODIS derived parameters for regional crop yield assessment [DOI]
  10. 10 Soil Science [DOI]
  11. 11 Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat [DOI]
  12. 12 Comparisons among Vegetation Indices and Bandwise Regression in a Highly Disturbed, Heterogeneous Landscape: Mount St. Helens, Washington [DOI]
  13. 13 Precision Farming: An Introduction [DOI]
  14. 14 Effect of Erosion and Landscape Position on the Productivity of Piedmont Soils [DOI]
  15. 15 Relationship Between Six Years of Corn Yields and Terrain Attributes [DOI]
  16. 16 Application of Meteosat derived meteorological information for crop yield predictions in Europe [DOI]
  17. 17 Relationship of landscape position and properties to crop production [DOI]
  18. 18 Land‐cover mixing and spectral vegetation indices [DOI]
  19. 19 Soil Productivity and Erosion of Iowa Loess Soils [DOI]
  20. 20 A framework for land evaluation

Cited by 22

Applicability of Monitoring Peanut Reflectance Using Hyperspectral Data for Precision Agriculture in East Nile Delta, Egypt

M. M. El-Sharkawy, E. Farg, S. M. Arafat, M. S. Abd El-Wahed · Journal of Agriculture and Ecology Research International · 2016

Integration of Big Data and Advanced Remote Sensing Techniques to Manage Field Irrigation in Arid Lands

Mohamed M. Elsharkawy, Mohsen Nabil, Eslam Farg · Springer Proceedings in Earth and Environmental Sciences · 2023

Agro-management Techniques and Precision Agriculture/Agriculture 4.0 in Arid Lands

Abdel-Ghany M. El-Gindy, Abdel-Aziz S. Sheta, Theib Y. Oweis · CABI Climate Change Series · 2024

Navigating Climate Change Challenges for a Resilient Future in Groundnut Production: A Review

Aaradhana Chilwal, Shivani Kothiyal, C. S. Praharaj · Journal of Agronomy and Crop Science · 2025

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Renan Falcioni, Werner Camargos Antunes, José Alexandre M. Demattê · Plants · 2023

Site-specific Crop Nutrient Management for Precision Agriculture – A Review

Tapan Gorai, Pankaj Kumar Yadav, Gopal Lal Choudhary · Current Journal of Applied Science and Technology · 2021

Optimal flight height and spectral indices for detecting insect injury in peanut crops using UAS

Jose Ricardo Lima Pinto, Caio Vinicius Gomes Pinhal, Odair Aparecido Fernandes · International Journal of Remote Sensing · 2025

Spectral method for macro and micronutrient prediction in soybean leaves using interval partial least squares regression

Glaucio Leboso Alemparte Abrantes dos Santos, Amanda Silveira Reis, Marcos Renan Besen · European Journal of Agronomy · 2023

Showing 8 of 22 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

61

Page views

0

PDF downloads

35

Outbound clicks

22

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.