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Review Articles

Vol. 12 No. 3 (2025)

Harnessing remote sensing for smart agriculture

DOI
https://doi.org/10.14719/pst.9174
Submitted
29 April 2025
Published
12-07-2025 — Updated on 22-07-2025
Versions

Abstract

Remote sensing technologies are transforming smart agriculture by delivering real-time data for decision-making in several spheres of crop health monitoring, precision irrigation, soil analysis and pest control. Crop growth stage monitoring, disease diagnosis and measurement of soil moisture are all made possible through these technologies which rely on advanced image processing algorithms and machine learning techniques. With this integration, farmers can implement precision agriculture practices, which in turn reduces resource waste and maximizes crop yields. Geographic information system (GIS) is also used to create detailed maps of agricultural areas, enabling the implementation of location-specific management practices. However, there are significant barriers that need to be addressed, including the requirement for high -resolution data, weather dependency and the need for technical capability. Despite these challenges, remote sensing technology has the potential to significantly improve agricultural productivity and sustainability. It is expected that further developments in remote sensing technology will lead to extensive application of the technology as well as tremendous impact on the agriculture sector.

References

  1. 1. Cohen CJ. Early history of remote sensing. In: Proceedings of 29th Applied Imagery Pattern Recognition Workshop. IEEE Computer Society; 2000. p. 3. https://doi.org/10.1109/AIPRW.2000.953595
  2. 2. Navalgund RR, Jayaraman V, Roy PS. Remote sensing applications: An overview. Curr Sci. 2007;25:1747-66.
  3. 3. Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 2019;24(2):152-64. https://doi.org/10.1016/j.tplants.2018.11.007
  4. 4. Jackson RD. Remote sensing of vegetation characteristics for farm management. Proc. SPIE 0475, Remote sensing: Critical review of technology. 1984. p. 81-97. https://doi.org/10.1117/12.966243
  5. 5. Surendran U, Nagakumar KC, Samuel MP. Remote sensing in precision agriculture. In: Digital agriculture: A solution for sustainable food and nutritional security. Cham: Springer International Publishing. 2024. p. 201-23. https://doi.org/10.1007/978-3-031-43548-5_7
  6. 6. Sreekantha DD, Rao KP. Applications of unmanned ariel vehicles (UAV) in agriculture: A study. Int J Res Appl Sci Eng. 2018;6:1162-66. https://doi.org/10.22214/ijraset.2018.5188
  7. 7. Ellenberg A, Branco L, Krick A, Bartoli I, Kontsos A. Use of unmanned aerial vehicle for quantitative infrastructure evaluation. J Infra Syst. 2015;21(3):04014054. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000246
  8. 8. Saeed IA, Qinglan S, Wang M, Butt SL, Zheng L, Tuan VN, et al. Development of a low-cost multi-depth real-time soil moisture sensor using time division multiplexing approach. IEEE Access. 2019;7:19688–97. https://doi.org/10.1109/ACCESS.2019.2893680
  9. 9. Jindo K, Kozan O, Iseki K, Maestrini B, van Evert FK, Wubengeda Y, et al. Potential utilization of satellite remote sensing for field-based agricultural studies. Chem Biol Technol Agric. 2021;8:1–6. https://doi.org/10.1186/s40538-021-00253-4
  10. 10. Martos V, Ahmad A, Cartujo P, Ordoñez J. Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Appl Sci. 2021;11(13):5911. https://doi.org/10.3390/app11135911
  11. 11. Jia Y, Su Z, Shen W, Yuan J, Xu Z. UAV technology and its application in agriculture. Adv Sci Tech Lett. 2016;137:107-11. https://doi.org/10.14257/astl.2016.137.20
  12. 12. Vaddi R, Kumar MA, Boggavarapu LNP. A survey on electromagnetic radiation based remote sensing applications to agriculture. Proceedings of 3rd International Conference of Intelligent Sustainable Systems (ICISS) 2020. Thoothukudi: India; 2020. p. 1197–202. https://doi.org/10.1109/ICISS49785.2020.9316095
  13. 13. Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, Walther S, Duveiller G, Cescatti A, et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci Adv. 2021;7(9):1–11. https://doi.org/10.1126/sciadv.abc7447
  14. 14. Ennouri K, Kallel A, Albano R. Remote sensing: An advanced technique for crop condition assessment. Math Probl Eng. 2019;9404565. https://doi.org/10.1155/2019/9404565
  15. 15. Falco N, Wainwright HM, Dafflon B, Ulrich C, Soom F, Peterson JE, et al. Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery. Sci Rep. 2021;11(1):7046. https://doi.org/10.1038/s41598-021-86480-z
  16. 16. Abd El Kareem Gomaa H. Modern trends in the development of smart agriculture projects. Int J Modern Agric Environ. 2022;2(1):33-44. https://doi.org/10.21608/ijmae.2023.214684.1000
  17. 17. Misra T. Indian remote sensing sensor system: Current and future perspective. Proc National Acad Sci India: Phys Sci. 201787:473-86. https://doi.org/10.1007/s40010-017-0429-7
  18. 18. Maleki H, McKenzie J. Review of US subsidence monitoring using conventional and satellite based methods. In: IOP conference series: Earth and environmental science. IOP Publishing. 2021;833(1):012154. https://doi.org/10.1088/1755-1315/833/1/012154
  19. 19. He J, Xu L, Xu D, Yu S, Wang K, Chang L. Satellite control and data processing unit software design based on multi-core processor. In: 2020 International conference on sensing, measurement & data analytics in the era of artificial intelligence (ICSMD). 2020; p. 352-56. https://doi.org/10.1109/ICSMD50554.2020.9261693
  20. 20. Choi YW, Yang SU, Kang MS, Kim EE. Development of TMA-based imaging system for hyperspectral application. In: International conference on space optics—ICSO 2008. 2017;10566:542-47. https://doi.org/10.1117/12.2308274
  21. 21. Skakun S, Kalecinski NI, Brown MG, Johnson DM, Vermote EF, Roger JC, et al. Assessing within-field corn and soybean yield variability from world view-3, planet, sentinel-2, and landsat 8 satellite imagery. Remote Sens. 2021;13(5):872. https://doi.org/10.3390/rs13050872
  22. 22. Yang C. High resolution satellite imaging sensors for precision agriculture. Front Agric Sci Eng. 2018;5(4):393-405. https://doi.org/10.15302/J-FASE-2018226
  23. 23. Segarra J, Araus JL, Kefauver SC. Farming and earth observation: Sentinel-2 data to estimate within-field wheat grain yield. Int J Appl Earth Obs Geoinf. 2022;1(107):102697. https://doi.org/10.1016/j.jag.2022.102697
  24. 24. Dakir A, Zahra BF, Omar AB. Optical satellite images services for precision agricultural use: A review. Adv Sci Technol Eng Syst J. 2021;6(3):326-31. https://doi.org/10.25046/aj060337
  25. 25. Jafarbiglu H, Pourreza A. A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. Comput Electron Agric. 2022;197:106844. https://doi.org/10.1016/j.compag.2022.106844
  26. 26. Vreugdenhil M, Greimeister-Pfeil I, Preimesberger W, Camici S, Dorigo W, Enenkel M, et al. Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges. Front Water. 2022;4:1045451. https://doi.org/10.3389/frwa.2022.1045451
  27. 27. Aziz D, Rafiq S, Saini P, Ahad I, Gonal B, Rehman SA, et al. Remote sensing and artificial intelligence: Revolutionizing pest management in agriculture. Front Sustain Food Syst. 2025;9:1551460. https://doi.org/10.3389/fsufs.2025.1551460
  28. 28. Wild KJ, Schmiedel T, Schueller JK. Concept for using unmanned aerial vehicles for a continuous provision of information for online application in precision farming. In: 2017 ASABE annual international meeting. Trans ASABE. 2017. p. 1. https://doi.org/10.13031/aim.201700334
  29. 29. Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I. A compilation of UAV applications for precision agriculture. Computer Networks. 2020;172:107148. https://doi.org/10.1016/j.comnet.2020.107148
  30. 30. Hassler SC, Baysal-Gurel F. Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy. 2019;9(10):618. https://doi.org/10.3390/agronomy9100618
  31. 31. Adhikary S, Biswas B, Naskar MK, Mukherjee B, Singh AP, Atta K. Remote sensing for agricultural applications. In: Arid environment-perspectives, challenges and management. Intech Open. 2022. https://doi.org/10.5772/intechopen.106876
  32. 32. Mehedi IM, Hanif MS, Bilal M, Vellingiri MT, Palaniswamy T. Remote sensing and decision support system applications in precision agriculture: Challenges and possibilities. IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3380830
  33. 33. Ashraf A, Ahmad L, Ferooz K, Ramzan S, Ashraf I, Khan JN, et al. Remote sensing as a management and monitoring tool for agriculture: Potential applications. Int J Environ Clim Chang. 2023;13:324-43. https://doi.org/10.9734/ijecc/2023/v13i81957
  34. 34. Virnodkar SS, Pachghare VK, Patil VC, Jha SK. Remote sensing and machine learning for crop water stress determination in various crops: A critical review. PA. 2020;21(5):1121-55. https://doi.org/10.1007/s11119-020-09711-9
  35. 35. Wang A, Zhang W, Wei X. A review on weed detection using ground-based machine vision and image processing techniques. Comput Electron Agric. 2019;158:226-40. https://doi.org/10.1016/j.compag.2019.02.005
  36. 36. Ahmad U, Alvino A, Marino S. A review of crop water stress assessment using remote sensing. Remote Sens. 2021;13(20):4155. https://doi.org/10.3390/rs13204155
  37. 37. Rovira-Más F, Saiz-Rubio V, Cuenca-Cuenca A. Sensing architecture for terrestrial crop monitoring: Harvesting data as an asset. Sensors. 2021;21(9):3114. https://doi.org/10.3390/s21093114
  38. 38. Molin JP, Tavares TR. Sensor systems for mapping soil fertility attributes: Challenges, advances, and perspectives in Brazilian tropical soils. Engenharia Agrícola. 2019;39:126-47. https://doi.org/10.1590/1809-4430 eng.agric.v39nep126-147/2019
  39. 39. Sui R. Irrigation scheduling using soil moisture sensors. J Agric Sci. 2017;10(1):1. https://doi.org/10.5539/jas.v10n1p1
  40. 40. Comegna A, Di Prima S, Hassan SB, Coppola A. A novel time domain reflectometry (TDR) system for water content estimation in soils: Development and application. Sensors. 2025;25(4):1099. https://doi.org/10.3390/s25041099
  41. 41. He H, Turner NC, Aogu K, Dyck M, Feng H, Si B, et al. Time and frequency domain reflectometry for the measurement of tree stem water content: A review, evaluation, and future perspectives. Agric For Meteorol. 2021;306:108442. https://doi.org/10.1016/j.agrformet.2021.108442
  42. 42. Wang Z, Che T, Zhao T, Dai L, Li X, Wigneron JP. Evaluation of SMAP, SMOS, and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:8955-70. https://doi.org/10.1109/JSTARS.2021.3108432
  43. 43. Singh G, Das NN, Panda RK, Mohanty BP, Entekhabi D, Bhattacharya BK. Soil moisture retrieval using SMAP L-band radiometer and RISAT-1 C-band SAR data in the paddy dominated tropical region of India. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:10644-64. https://doi.org/10.1109/JSTARS.2021.3117273
  44. 44. Marín-Ortiz JC, Gutierrez-Toro N, Botero-Fernández V, Hoyos-Carvajal LM. Linking physiological parameters with visible/near-infrared leaf reflectance in the incubation period of vascular wilt disease. Saudi J Biol Sci. 2020;27(1):88-99. https://doi.org/10.1016/j.sjbs.2019.05.007
  45. 45. Li ZL, Wu H, Duan SB, Zhao W, Ren H, Liu X, et al. Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications. Rev Geophys. 2023;61(1): e2022RG000777. https://doi.org/10.1029/2022RG000777
  46. 46. Kumar D, Soni A, Kumar M. Retrieval of land surface temperature from landsat-8 thermal infrared sensor data. J Hum Earth Future. 2022;3(2):159-68. https://doi.org/10.28991/HEF-2022-03-02-02
  47. 47. Xue J, Anderson MC, Gao F, Hain C, Knipper KR, Yang Y, et al. Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion. Irrig Sci. 2022;40(4):609–34. https://doi.org/10.1007/s00271-022-00799-7
  48. 48. Chakraborty R, Rachdi I, Thiele S, Booysen R, Kirsch M, Lorenz S, et al. A spectral and spatial comparison of satellite-based hyperspectral data for geological mapping. Remote Sens. 2024;16(12):2089. https://doi.org/10.3390/rs16122089
  49. 49. Ferner J, Linstädter A, Rogass C, Südekum KH, Schmidtlein S. Towards forage resource monitoring in subtropical Savanna grasslands: Going multispectral or hyperspectral? Eur J Remote Sens. 2021;54(1):364-84. https://doi.org/10.1080/22797254.2021.1934556
  50. 50. Darwish N, Kaiser M, Koch M, Gaber A. Assessing the accuracy of ALOS/PALSAR-2 and sentinel-1 radar images in estimating the land subsidence of coastal areas: A case study in Alexandria city, Egypt. Remote Sens. 2021;13(9):1838. https://doi.org/10.3390/rs13091838
  51. 51. Angelopoulou T, Chabrillat S, Pignatti S, Milewski R, Karyotis K, Brell M, et al. Evaluation of airborne hyspex and spaceborne PRISMA hyperspectral remote sensing data for soil organic matter and carbonates estimation. Remote Sens. 2023;15(4):1106. https://doi.org/10.3390/rs15041106
  52. 52. Xu X, Du C, Ma F, Qiu Z, Zhou J. A framework for high-resolution mapping of soil organic matter (SOM) by the integration of fourier mid-infrared attenuation total reflectance spectroscopy (FTIR-ATR), sentinel-2 images, and DEM derivatives. Remote Sens. 2023;15(4):1072. https://doi.org/10.3390/rs15041072
  53. 53. Misbah K, Laamrani A, Khechba K, Dhiba D, Chehbouni A. Multi-sensors remote sensing applications for assessing, monitoring, and mapping NPK content in soil and crops in African agricultural land. Remote Sens. 2021;14(1):81. https://doi.org/10.3390/rs14010081
  54. 54. Peng X, Chen D, Zhou Z, Zhang Z, Xu C, Zha Q, et al. Prediction of the nitrogen, phosphorus and potassium contents in grape leaves at different growth stages based on UAV multispectral remote sensing. Remote Sens. 2022;14(11):2659. https://doi.org/10.3390/rs14112659
  55. 55. Mzid N, Castaldi F, Tolomio M, Pascucci S, Casa R, Pignatti S. Evaluation of agricultural bare soil properties retrieval from Landsat 8, Sentinel-2 and PRISMA satellite data. Remote Sens. 2022;14(3):714. https://doi.org/10.3390/rs14030714
  56. 56. Vellingiri A, Kokila R, Nisha P, Kumar M, Chinnusamy S, Boopathi S. Harnessing GPS, sensors, and drones to minimize environmental impact: Precision agriculture. In: Designing sustainable internet of things solutions for smart industries. IGI Global. 2025; p. 77-108. https://doi.org/10.4018/979-8-3693-5498-8.ch004
  57. 57. Monteiro A, Santos S. Sustainable approach to weed management: The role of precision weed management. Agron. 2022;12(1):118. https://doi.org/10.3390/agronomy12010118
  58. 58. Jussaume Jr RA, Dentzman K, Frisvold G, Ervin D, Owen M. Factors that influence on-farm decision-making: Evidence from weed management. Soc Nat Resour. 2022;35(5):527-46. https://doi.org/10.1080/08941920.2021.2001123
  59. 59. Xuan TD, Khanh TD, Minh TT. Implementation of conventional and smart weed management strategies in sustainable agricultural production. Weed Biol Manag. 2025;25(1):e70000. https://doi.org/10.1111/wbm.70000
  60. 60. Phang SK, Chiang TH, Happonen A, Chang MM. From satellite to UAV-based remote sensing: A review on precision agriculture. IEEE Access. 2023;11:127057-76. https://doi.org/10.1109/ACCESS.2023.3330886
  61. 61. Gerhards M, Schlerf M, Mallick K, Udelhoven T. Challenges and future perspectives of multi-/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens. 2019;11(10):1240. https://doi.org/10.3390/rs11101240
  62. 62. Gogoi NK, Deka B, Bora LC. Remote sensing and its use in detection and monitoring plant diseases: A review. Agric Rev. 2018;39(4):307-13. https://doi.org/10.18805/ag.R-1835
  63. 63. Acharya MC, Thapa RB. Remote sensing and its application in agricultural pest management. J Agric Environ. 2015;16:43-61. https://doi.org/10.3126/aej.v16i0.19839
  64. 64. Chen X, Shi D, Zhang H, Pérez JA, Yang X, Li M. Early diagnosis of greenhouse cucumber downy mildew in seedling stage using chlorophyll fluorescence imaging technology. Biosyst Eng. 2024;242:107-22. https://doi.org/10.1016/j.biosystemseng.2024.04.013
  65. 65. Chaudhary M, Sahu P, Singh K, Kumar R, Shukla A, Singh C, et al. Effect of humidity on pest and disease incidence in crops. In: Climate change and biotic factors. Apple Academic Press. 2025. p. 3-42. https://doi.org/10.1201/9781003568704-2
  66. 66. Bernardo S, Dinis LT, Machado N, Moutinho-Pereira J. Grapevine abiotic stress assessment and search for sustainable adaptation strategies in Mediterranean-like climates. A review. Agron Sustain Dev. 2018;38(6):66. https://doi.org/10.1007/s13593-018-0544-0
  67. 67. Hall RJ, Castilla G, White JC, Cooke BJ, Skakun RS. Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective. Can Entomol. 2016;148(S1):S296-356. https://doi.org/10.4039/tce.2016.11
  68. 68. Sabtu NM, Idris NH, Ishak MH. The role of geospatial in plant pests and diseases: An overview. In: IOP conference series: Earth and environmental science. IOP Publishing. 2018;169(1):012013. https://doi.org/10.1088/1755-1315/169/1/012013
  69. 69. Feng A, Zhou J, Vories ED, Sudduth KA, Zhang M. Yield estimation in cotton using UAV-based multi-sensor imagery. Biosyst Eng. 2020;193:101-14. https://doi.org/10.1016/j.biosystemseng.2020.02.014
  70. 70. Toscano P, Castrignanò A, Di Gennaro SF, Vonella AV, Ventrella D, Matese A. A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping. Agron. 2019;9(8):437. https://doi.org/10.3390/agronomy9080437
  71. 71. Olson D, Anderson J. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agron. 2021;113(2):971-92. https://doi.org/10.1002/agj2.20595
  72. 72. Ansarifar J, Wang L, Archontoulis SV. An interaction regression model for crop yield prediction. Sci Rep. 2021;11(1):17754. https://doi.org/10.1038/s41598-021-97221-7
  73. 73. Huber F, Inderka A, Steinhage V. Leveraging remote sensing data for yield prediction with deep transfer learning. Sensors. 2024;24(3):770. https://doi.org/10.3390/s24030770
  74. 74. Pazhanivelan S, Geethalakshmi V, Tamilmounika R, Sudarmanian NS, Kaliaperumal R, Ramalingam K, et al. Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model. Agron. 2022;12(9):2008. https://doi.org/10.3390/agronomy12092008
  75. 75. Zhao D, Raja Reddy K, Kakani VG, Read JJ, Carter GA. Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant Soil. 2003;257:205-18. https://doi.org/10.1023/A:1026233732507
  76. 76. Zhao P, Bai Y, Zhang Z, Wang L, Guo J, Wang J. Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models. Ecol Inform. 2023;73:101948. https://doi.org/10.1016/j.ecoinf.2022.101948
  77. 77. Yousfi S, Peira JF, De La Horra GR, Ablanque PV. Remote sensing: Useful approach for crop nitrogen management and sustainable agriculture. In: Sustainable crop production. Intech Open. 2019. https://doi.org/10.5772/intechopen.89422
  78. 78. Silva L, Conceição LA, Lidon FC, Maçãs B. Remote monitoring of crop nitrogen nutrition to adjust crop models: A review. Agriculture. 2023;13(4):835. https://doi.org/10.3390/agriculture13040835
  79. 79. Moges SM, Raun WR, Mullen RW, Freeman KW, Johnson GV, Solie JB. Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield. J Plant Nutr. 2005;27(8):1431-41.
  80. 80. Ma J, Cheng J, Wang J, Pan R, He F, Yan L, Xiao J. Rapid detection of total nitrogen content in soil based on hyperspectral technology. Inf Process Agric. 2022;9(4):566-74. https://doi.org/10.1016/j.inpa.2021.06.005
  81. 81. Didal V, Vidyasagar G, Mahender Kumar R, Surekha K, Narender Reddy S, Bhooshan B. Effect of nitrogen management practices on SPAD values and NDVI readings of rice crop. Pharma Innov J. 2022;11:367-71.
  82. 82. Huang S, Tang L, Hupy JP, Wang Y, Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res. 2021;32(1):1-6. https://doi.org/10.1007/s11676-020-01155-1
  83. 83. Sharifi A, Felegari S. Remotely sensed normalized difference red-edge index for rangeland biomass estimation. AEAT. 2023;95(7):1128-36. https://doi.org/10.1108/AEAT-07-2022-0199
  84. 84. Vincini M, Frazzi ER, D’Alessio PA. A broad-band leaf chlorophyll vegetation index at the canopy scale. PA. 2008;9:303-19. https://doi.org/10.1007/s11119-008-9075-z
  85. 85. Kaya Y, Polat N. A linear approach for wheat yield prediction by using different spectral vegetation indices. Int J Eng Geosci. 2023;8(1):52-62. https://doi.org/10.26833/ijeg.1035037
  86. 86. Richard JU, Abah IA. Derivation of land surface temperature (LST) from Landsat 7 & 8 imageries and its relationship with two vegetation indices (NDVI and GNDVI). Int J Res Granthaalayah. 2019;7(2):108-20. https://doi.org/10.29121/granthaalayah.v7.i2.2019.1013
  87. 87. Zhang H, Li J, Liu Q, Lin S, Huete A, Liu L, et al. A novel red‐edge spectral index for retrieving the leaf chlorophyll content. Methods Ecol Evol. 2022;13(12):2771-87. https://doi.org/10.1111/2041-210X.13994
  88. 88. Srivastava PK, Gupta M, Singh U, Prasad R, Pandey PC, Raghubanshi AS, et al. Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data. Environ Dev Sustain. 2021;23:5504-19. https://doi.org/10.1007/s10668-020-00827-6
  89. 89. Szabó A, Tamás J, Nagy A. Spectral estimation of chlorophyll for non-invasive assessment in apple orchards. Hortic. 2024;10(12):1266. https://doi.org/10.3390/horticulturae10121266
  90. 90. Vij A, Vijendra S, Jain A, Bajaj S, Bassi A, Sharma A. IoT and machine learning approaches for automation of farm irrigation system. Procedia Comput Sci. 2020;167:1250-57. https://doi.org/10.1016/j.procs.2020.03.440
  91. 91. Ismail N, Rajendran S, Tak WC, Xin TK, Anuar NS, Zakaria FA, et al. Smart irrigation system based on internet of things (IOT). In: Journal of Physics: Conference Series. IOP Publishing. 2019;1339(1):012012. https://doi.org/10.1088/1742-6596/1339/1/012012

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