The Recent Applications of Remote sensing in Agriculture-A Review
Main Article Content
Abstract
Remote sensing is becoming a crucial technology in current agricultural practices, with several uses and benefits for farmers, researchers and policymakers. Crop monitoring and management are the principal applications of remote sensing in agriculture. Remote sensing allows for the rapid and precise diagnosis of crop health, growth and yield estimation by evaluating data received from satellites or airborne platforms. This data assists farmers in optimising irrigation, fertilization, pest and disease control measures, resulting in better resource allocation, enhanced productivity and lower environmental consequences. The identification and mapping of crop diseases and pests is a key application. Remote sensing may detect minute differences in plant physiology, such as chlorophyll content changes, which may signal the presence of diseases or pest infestations. Initial identification allows for focused treatments such as precision pesticide application, disease avoidance and crop loss reduction. Precision agriculture relies heavily on remote sensing. Farmers may produce precise field maps that delineate differences in soil qualities, nutrient levels, and moisture content by integrating satellite photography, GPS navigation systems and computer algorithms. This data enables site-specific management, allowing farmers to deploy resources precisely where they are required, optimising inputs, lowering costs and minimising environmental consequences. Remote sensing makes land-use planning and monitoring easier. It can assist in identifying potential agricultural sites, assessing land degradation and tracking changes in land cover and land use trends over time. Policymakers can use this data to make informed decisions about land management, sustainable agriculture practices and conservation activities. It helps with agricultural water resource management. It is feasible to monitor water availability, assess irrigation demands and identify locations vulnerable to drought or water stress by studying satellite data. This information allows for more efficient water distribution, reducing water waste and improving water-use efficiency in agricultural activities. Remote sensing has numerous uses in agriculture, revolutionizing old farming practices.
Keywords: Artificial intelligence, precision agriculture, remote sensing, satellites, spectral reflectance, sustainability
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Attribution: You must credit the original creator, provide a link to the license, and indicate if you made changes. You can do this in any reasonable way, but you can't suggest that the original creator endorses you or your use.
- NonCommercial: You can't use the material for commercial purposes.
- NoDerivatives: If you remix, transform, or build upon the material, you can't distribute the modified material.
- You must own or control the copyright to the work.
- You can't revoke a CC license.
- Anyone who receives the material can rely on the license as long as the material is protected by copyright.
- If you created a work as part of your job, you might not own the copyright.
How to Cite
References
Abdalzaher MS, Elsayed HA, Fouda MM, Salim MM (2023) Employing machine learning and IoT for earthquake early
warning system in smart cities. Energies 16:495.
https://doi.org/10.3390/en16010495
Alarifi SS, Abdelkareem M, Abdalla F, Alotaibi M (2022) Flash flood hazard mapping using remote sensing and GIS
techniques in southwestern Saudi Arabia. Sustainability 14:14145.
https://doi.org/10.3390/su142114145
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ diseases using EO-1 Hyperion hyperspectral
imagery. Int J Remote Sens 25:489-498.
https://doi.org/10.1080/01431160310001618031
Baumhoer CA, Dietz AJ, Dech S, Kuenzer C (2018) Remote sensing of Antarctic glacier and ice-shelf front dynamics-A review.
Remote Sensing 10:1445.
https://doi.org/10.3390/rs10091445
Brocca L, Tarpanelli A, Filippucci P, Dorigo W, Zaussinger F, Gruber A, Prieto DF (2018) How much water is used for
irrigation? A new approach exploiting coarse resolution satellite soil moisture products. Int J Appl Earth Obs Geoinf 73:752-766.
https://doi.org/10.1016/j.jag.2018.08.023
Calantropio A, Chiabrando F, Sammartano G, Spano A, Lose LT (2018) UAV strategies validation and remote sensing data for damage assessment in post-disaster scenarios. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42:121-128.
https://doi.org/10.5194/isprs-archives-XLII-3-W4-121-2018
Castillejo-Gonzalez IL, Pena-Barragan JM, Jurado-Exposito M, Mesas-Carrascosa FJ, Lopez-Granados F (2014) Evaluation of pixel-and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management. Eur J Agron 59:57-66.
https://doi.org/10.1016/j.eja.2014.05.009
Damasevicius R, Bacanin N., Misra S (2023) From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management. Journal of Sensor and Actuator Networks 12(3): 41.
https://doi.org/10.3390/jsan12030041
Ghaffarian S, Roy D, Filatova T, Kerle N (2021) Agent-based modelling of post-disaster recovery with remote sensing data. International Journal of Disaster Risk Reduction 60:102285.
https://doi.org/10.1016/j.ijdrr.2021.102285
Huang W, Guan Q, Luo J, Zhang J, Zhao J, Liang D, Huang L, Zhang D (2014) New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2516-2524.
doi:10.1109/JSTARS.2013.2294961.
Hunt ERJ, Daughtry, CS (2018) What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International Journal of Remote Sensing 39:5345-5376.
https://doi.org/10.1080/01431161.2017.1410300
Hussain S, Karuppannan S (2023) Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan. Geology Ecology and Landscapes 7:46-58.
https://doi.org/10.1080/24749508.2021.1923272
Jalilvand E, Tajrishy M, Hashemi SAGZ, Brocca L (2019) Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sens Environ 231:111226. https://doi.org/10.1016/j.rse.2019.111226
Janekovic I, Rayson MD, Jones NL, Watson P, Pattiaratchi C (2022) 4D-Var data assimilation using satellite sea surface temperature to improve the tidally-driven interior ocean dynamics estimates in the Indo-Australian Basin. Ocean Modelling 171:101969.
https://doi.org/10.1016/j.ocemod.2022.101969
Jiao W, Wang L, McCabe MF (2021) Multi-sensor remote sensing for drought characterization: current status, opportunities
and a roadmap for the future. Remote Sensing of Environment 256:112313.
https://doi.org/10.1016/j.rse.2021.112313
Khanal S, Fulton J, Shearer S (2017) An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture 139:22-32. https://doi.org/10.1016/j.compag.2017.05.001
Kumawat RK, Tiwari G, Ramakrishnan RS, Bhayal D, Debnath S, Thakur S, Bhayal L (2023) Remote Sensing Related Tools and their Spectral Indices Applications for Crop Management in Precision Agriculture. International Journal of Environment and Climate Change 13:171-188.
https://doi.org/10.9734/ijecc/2023/v13i11665
Luo J, Huang W, Zhao J, Zhang J, Zhao C, Ma R (2013) Detecting aphid density of winter wheat leaf using hyperspectral measurements. IEEE J Sel Top Appl Earth Obs Remote Sens 6:690-698. doi: 10.1109/JSTARS.2013.2248345
Oerke EC (2006) Crop losses to pests. The Journal of Agricultural Science 144:31-43. https://doi.org/10.1017/S0021859605005708
Prabhakar M, Prasad Y, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B (2011) Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput Electron Agric 79:189-198.
https://doi.org/10.1016/j.compag.2011.09.012
Rebouh NY, Mohamed ES, Polityko PM, Dokukin PA, Kucher DE, Latati M, Okeke SE, Ali MA (2023) Towards improving the precision agriculture management of the wheat crop using remote sensing: A case study in the Central Non-Black Earth region of Russia. The Egyptian Journal of Remote
Sensing and Space Science 26:505-517. https://doi.org/10.1016/j.ejrs.2023.06.007
Samreen T, Ahmad M, Baig MT, Kanwal S, Nazir MZ (2023) Remote Sensing in Precision Agriculture for Irrigation Management. Environmental Sciences Proceedings 23:31. https://doi.org/10.3390/environsciproc2022023031
Saranya T, Deisy C, Sridevi S, Anbananthen KSM (2023) A comparative study of deep learning and Internet of Things for precision agriculture. Engineering Applications of Artificial Intelligence 122:106034.
https://doi.org/10.1016/j.engappai.2023.106034 Sharma TPP, Zhang J, Koju UA, Zhang S, Bai Y, Suwal MK (2019) Review of flood disaster studies in Nepal: A remote sensing perspective. International Journal of Disaster Risk Reduction 34:18-27.
https://doi.org/10.1016/j.ijdrr.2018.11.022
Sishodia RP, Ray RL, Singh SK (2020) Applications of remote sensing in precision agriculture: A review. Remote Sensing 12:3136. https://doi.org/10.3390/rs12193136
Sun M, Gong A, Zhao X, Liu N, Si L, Zhao S (2023) Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology. Remote Sensing 15:3353. Sun M, Gong A, Zhao X, Liu N, Si L, Zhao S (2023) Reconstruction of a Monthly 1 km NDVI Time
Series Product in China Using Random Forest Methodology. Remote Sensing 15:3353.
Tran DQ, Park M, Jung D, Park, S. (2020) Damage-map estimation using UAV images and deep learning algorithms for disaster management system. Remote Sensing 12:4169. https://doi.org/10.3390/rs12244169
Wei X, Chang NB, Bai K, Gao, W (2020) Satellite remote sensing of aerosol optical depth: Advances, challenges, and perspectives. Critical Reviews in Environmental Science and Technology 50:1640-1725. https://doi.org/10.1080/10643389.2019.1665944
Zhang X, Qiu J, Leng G, Yang Y, Gao Q, Fan Y, Luo, J (2018) The potential utility of satellite soil moisture retrievals for detecting irrigation patterns in China. Water, 10:1505. https://doi.org/10.3390/w10111505
Zheng G, Muhammad S, Sattar A, Ballesteros-Canovas JA (2023) Cryospheric remote sensing. Frontiers in Remote Sensing
4:1204667. https://doi.org/10.3389/frsen.2023.1204667