• Prince Darji Gujarat University
  • Nirmal Desai
  • Dhara Bhavsar
  • Himanshu Pandya




Agriculture, Remote sensing, Precision Agriculture


Agriculture plays a central role in safeguarding the region’s food supply and achieving the second UN Sustainable Development Goal of zero hunger by 2030. However, the agriculture sector faces challenges from changing consumer demand, demographics, inefficient value chains, climate change and water shortage. Climate change is already impacting significantly on agriculture and food production in developing countries. Agriculture has been able to keep up with the rising demand for food and other agricultural goods because of the development of new farming techniques throughout the previous century. Natural resources will undoubtedly be further stressed as result of rising food demand, population growth, income levels, etc. New methods and approaches should be able to meet future food demands while maintaining or lowering agriculture’s environmental imprint as the detrimental effects of agriculture on the environment become more widely acknowledged.

The application of remote sensing in agriculture can aid the evolution of agricultural practices that face different types of challenges by providing information related to crop status at different scales all through the season. Making educated management decisions with the help of emerging technologies including geospatial technology, the Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI). To maximize agricultural inputs, boot agricultural production, and decrease input losses, precision agriculture (PA) uses a variety of such technologies. Over the past few decades, there has been a sharp expansion in the use of remote sensing technology for PA (precision agriculture). It is crucial to investigate and design an easy-to-use yet dependable workflow for the real-time use of remote sensing in PA (precision agriculture) given the complexity of image processing and the quantity of technical knowledge and skill required. Wider usage of remote sensing technologies in commercial and non-commercial PA (precision agriculture) applications is likely to result from the development of accurate yet simple-to-use, user-friendly systems.


Abdel-Hamid, M. A., Ahmed, A. E. M., & El-Metwally, M. (2018). GIS-based modeling for sustainable water management in agriculture: A review. Water, 10(3), 277. doi: 10.3390/w10030277

Anderson, M. C., Neale, C. M. U., Li, F., Norman, J. M., Kustas, W. P., Jayanthi, H., & Chavez, J. O. S. E. (2004). Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote sensing of environment, 92(4), 447-464.

Apostol, S., Viau, A. A., Tremblay, N., Briantais, J.-M., Prasher, S., Parent, L.-E., et al. (2003). Laser-induced fluorescence signatures as a tool for remote monitoring of water and nitrogen stresses in plants. Canadian Journal of Remote Sensing, 29, 57e65.

Ben-Dor, E. (2010). Characterization of soil properties using reflectance spectroscopy. Ch. 22. In P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705). Boca Raton, FL: CRC Press.

Berckmans, D., Vranken, E., &Aerts, J. M. (2019). Precision livestock farming technologies for welfare management in intensive livestock systems. Animal Frontiers, 9(1), 9-15.

Betbeder, J.; Fieuzal, R.; Baup, F. Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2540–2553

Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273e285.

Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 2013, 139, 231–245.

Casa, R., & Jones, H. G. (2005). LAI retrieval from multiangular image classification and inversion of a ray tracing model. Remote Sensing of Environment, 98(4), 414-428.

Castillo, A., Hoover, A., & Joy, A. (2018). Urban agriculture mapping: using GIS technology to quantify and analyze urban agriculture in US cities. Journal of Agriculture, Food Systems, and Community Development, 8(4), 61-76.Chung, E. J., Khan, M. A., & Kim, M. S. (2019). Application of GIS for tracking crop products: a review. Journal of the Korean Society for Applied Biological Chemistry, 62(1), 1-10.

Chakraborty, S., Singh, V. K., Dubey, R., & Singh, S. (2020). Crop classification using remote sensing data: A review. International Journal of Applied Earth Observation and Geoinformation, 91, 102149.

Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y., &Tsipris, J. (2005). Estimation of leaf water potential by thermal imagery and spatial analysis. Journal of Experimental Botany, 56, 1843e1852.

Dąbrowska-Zielińska, K., Ciołkosz, A., Budzyńska, M., &Kowalik, W. (2008). Monitorowaniewzrostuiplonowaniazbóżmetodamiteledetekcji. ProblemyInżynieriiRolniczej, 16(4), 45-54.

De Longueville, B., Tardy, A., & Bogaert, P. (2021). Combining GIS and blockchain to improve transparency in the food supply chain. Journal of Cleaner Production, 292, 126023. doi: 10.1016/j.jclepro.2021.126023

Deb, S. K., & Shukla, A. K. (2017). Mapping soil health for sustainable agriculture: a review. Environmental Science and Pollution Research, 24(20), 16559-16575.

Demetriades-Shah, T. H., Steven, M. D., & Clark, J. A. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 55e56.

Duan, L., Wang, J., Zhang, W., & Qin, Q. (2020). Application of remote sensing technology in agricultural sustainable development. Journal of Cleaner Production, 270, 122538.

Estes, J., & Jensen, J. (1998). Development of remote sensing digital image processing systems and raster GIS. The history of geographic information systems, 163-180.

Fernandez-Ordoñez, Y.M.; Soria-Ruiz, J. Maize crop yield estimation with remote sensing and empirical models. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; pp. 3035–3038

Folberth, C., Skalsky, R., Moltchanova, E., Balkovič, J., Azevedo, L. B., Obersteiner, M., ... & Schmid, E. (2016). Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nature Communications, 7, 11872.

Food and Agriculture Organization of the United Nations (FAO). Global Strategy to Improve Agricultural and Rural Statistics; Report No. 56719-GB; FAO: Rome, Italy, 2011.

Ghiasi, S. E., Alavipanah, S. K., &Saberian, M. (2021). Farm automation and robotics: review, application, and future trends. Computers and Electronics in Agriculture, 183, 106017.

Gholizadeh, A., Marofi, S., Khorramdel, S., Gholizadeh, H., &Yari, A. (2020). Crop yield prediction using remote sensing and machine learning techniques: A comprehensive review. Computers and Electronics in Agriculture, 175, 105565.

Gnädinger, F.; Schmidhalter, U. Digital counts of maize plants by Unmanned Aerial Vehicles (UAVs). Remote Sens. 2017, 9, 544

Goodman, L. A., & Kruskal, W. H. (1959). Measures of association for cross classifications. II: Further discussion and references. Journal of the American Statistical Association, 54(285), 123-163.

Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., &Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416e426.

Hassan-Esfahani, L.; Torres-Rua, A.; Jensen, A.; Mckee, M. Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery. Irrig. Drain. 2017, 66, 273–288.

Huang, W., Yang, Y., Sun, X., Zhang, J., Wang, Z., & Xu, Y. (2017). Remote sensing for crop water management: From ET modeling to practical applications for irrigation scheduling and drought monitoring in agricultural areas. Remote Sensing, 9(9), 888.

Huete, A. R., &Escadafal, R. (1991). Assessment of biophysical soil properties through spectral decomposition techniques. Remote Sensing of Environment, 35, 149e159.

Jiang, J., Zhou, K., Chen, Y., Ma, L., & Yuan, F. (2021). Remote sensing of crop classification: A review of research progress and perspectives. International Journal of Agricultural and Biological Engineering, 14(2), 1-15.

Jiang, Z., Chen, X., Shi, J., Wei, X., & Zhang, J. (2021). Crop classification based on deep learning: A review. Remote Sensing, 13(1), 161.

Jin, X.; Liu, S.; Baret, F.; Hemerlé, M.; Comar, A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 2017, 198, 105–114

Kalayeh, H. M., &Landgrebe, D. A. (1986). Utilizing multitemporal data by a stochastic model. IEEE transactions on geoscience and remote sensing, (5), 792-795.

Kamble, A. S., Kamble, S. S., & Sajeev, M. V. (2020). Remote sensing applications in agriculture and allied sectors: A review. Advances in Remote Sensing, 9(1), 1-17.

Kartika, D., Ismail, A. H., &Tilaar, H. A. (2021). Predictive analytics for crop yield prediction using geographic information system: a review. IOP Conference Series: Earth and Environmental Science, 791, 012049.

Khan, A. H., Jiang, Y., & Al-Yahyai, R. (2021). Application of machine learning techniques for crop yield prediction: A review. Computers and Electronics in Agriculture, 182, 106041.

Khanal, S.; Fulton, J.; Shearer, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 2017, 139, 22–32.

Lamb, D. W., & Brown, R. B. (2001). Pa—precision agriculture: Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research, 78(2), 117-125.

Liang, Y., Jiang, L., Wang, J., Gao, P., & Gao, W. (2021). Opportunities and challenges for using remote sensing technology in smart agriculture. Smart Agriculture, 3(1), 68-78.

Lu, D., Chen, Q., Wang, G., Liu, L., & Moran, E. (2019). A review of remote sensing applications in natural resource management and conservation. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 57- 65.

Lu, W., Zhu, Y., Sun, X., & Zhao, S. (2021). Remote sensing-based crop disease identification and diagnosis using machine learning: A review. Remote Sensing, 13(4), 669.

Luo, L., Ma, X., Liu, Y., Xu, X., Chen, Q., & Zhu, X. (2020). Crop growth monitoring and yield estimation based on machine learning techniques: A review. Agricultural and Forest Meteorology, 287, 107948.

Mendes, R., Portella, K. F., Godoi, W. C., Galvo, J. C. A., Joukoski, A., Martins, P., ... & de Geus, K. (2009). Determination of crushed stone volume in concrete cores from hydroelectric power plant dams by three-dimensional tomography. Insight-Non-Destructive Testing and Condition Monitoring, 51(12), 654-659.

Mishra, S., & Lal, R. (2017). Precision agriculture and soil carbon sequestration: A review. Environmental Development, 23, 77-89. doi: 10.1016/j.envdev.2017.04.002

Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319e346

Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; Hernández-Montes, E.; O’Connell, M. Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). RemoBte Sens. 2017, 9, 828.

Pinter, P. J., Jr., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., et al. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69, 647e664.

Reeves, M., & Miller, E. E. (1975). Estimating infiltration for erratic rainfall. Water Resources Research, 11(1), 102-110.

Rundquist, D., & Samson, S. (1983). Application of remote sensing in agricultural analysis. Chapter 15 in B. Richason, Jr., ed. Introduction to Remote Sensing of the Environment, 317-337.

Santos, M. A. S., Costa, M. C., & Sousa, J. J. (2020). Satellite-based techniques for detecting crop stress and their applications in precision agriculture. Applied Sciences, 10(18), 6374.

Shao, Y., Hu, Q., & Xiao, Q. (2019). Remote sensing applications in irrigation management: A review. Remote Sensing, 11(20), 2362.

Sripada, R. P., Schmidt, J. P., Dellinger, A. E., & Beegle, D. B. (2008). Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agronomy Journal, 100, 1553e1561.

Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of agricultural engineering research, 76(3), 267-275.

Stöcker, F., Albrecht, A., and Mußhoff, O. (2019). "Agroforestry mapping: A review of methods and applications," Land Use Policy, 81, 757-768.

Talukder, M. A. I., Masud, M. M., & Islam, M. R. (2019). GIS-based crop monitoring for precision agriculture. International Journal of Agricultural and Environmental Information Systems, 10(2), 1-16. doi: 10.4018/IJAEIS.2019040101

Talukder, T., Sathish, S., Garg, V., & Raut, S. (2019). Crop monitoring in precision agriculture using GIS and remote sensing: a review. Journal of Agrometeorology, 21(1), 29-35.

Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric engineering and remote sensing, 68(6), 607-622.

Thomasson, J. A., Sui, R., Cox, M. S., &AleRajehy, A. (2001). Soil reflectance sensing for determining soil properties in precision agriculture. Transactions of the ASAE, 44, 1445e1453

Tishechkin, A. K., Han, J., & Alekseev, A. V. (2016). The use of geographic information systems in the development of land use planning for sustainable agriculture. Eurasian Journal of Soil Science, 5(3), 216-224.

Tishechkin, A. K., Khabarov, N. V., &Obersteiner, M. (2016). Assessment of land-use change effects on soil organic carbon stocks in the spatially explicit farming system model EPIC using remote sensing data. Journal of Environmental Management, 183, 238-246. doi: 10.1016/j.jenvman.2016.08.057

Varela, S.; Dhodda, P.R.; Hsu, W.H.; Prasad, P.V.V.; Assefa, Y.; Peralta, N.R.; Griffin, T.; Sharda, A.; Ferguson, A.; Ciampitti, I.A. Early-season stand count determination in Corn via integration of imagery from unmanned aerial systems (UAS) and supervised learning techniques. Remote Sens. 2018, 10, 343

Von Fragstein, P., Li, J., Evers, J., & Kahl, J. (2019). Green farming systems for the future. In Sustainable Agriculture Reviews (pp. 69-87). Springer, Cham.

Wang, H., Zhang, Y., Ma, C., Liu, M., & Yao, Y. (2019). The application of remote sensing technology in the monitoring and management of agricultural water resources. Water, 11(7), 1471.

Wang, L., Chen, Y., Wei, J., Zhang, C., & Zhang, X. (2021). Hyperspectral imaging technology and its applications in agricultural production. Transactions of the ASABE, 64(1), 267-280.

Wirbel, M., Stech, B., & Bauer, M. (1985). Exclusive semileptonic decays of heavy mesons. Zeitschrift für Physik C Particles and Fields, 29(4), 637-642.

Yang, Y., Hu, X., Li, J., Huang, C., & Wang, Y. (2020). Precision agriculture based on remote sensing and machine learning: A review. Remote Sensing, 12(8), 1335.

Yao, X.; Wang, N.; Liu, Y.; Cheng, T.; Tian, Y.; Chen, Q.; Zhu, Y. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens. 2017, 9, 1304.

Yu, Q., Wu, B., Liu, L., Dong, Y., & Zhang, X. (2020). Review on precision agriculture research based on remote sensing. IEEE Access, 8, 32815-32829.

Yu, S., Tang, J., Wang, Y., Huang, H., & Wu, J. (2021). A review of decision support systems for irrigation management based on geographic information system. Journal of Agricultural Science and Technology, 23(3), 1-14.

Yu, Z.; Cao, Z.; Wu, X.; Bai, X.; Qin, Y.; Zhuo, W.; Xiao, Y.; Zhang, X.; Xue, H. Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage. Agric. For. Meteorol. 2013, 174, 65–84.

Zheng, L., Huang, Y., Fang, H., Sun, Z., & Chen, X. (2021). An overview of precision agriculture applications based on machine learning. Computers and Electronics in Agriculture, 181, 105946.




How to Cite

Darji, P., Desai, N., Bhavsar, D., & Pandya, H. (2023). A REVIEW : APPLICATIONS OF REMOTE SENSING IN AGRICULTURE. International Association of Biologicals and Computational Digest, 2(1), 108–117. https://doi.org/10.56588/iabcd.v2i1.137




Most read articles by the same author(s)

1 2 3 > >>