Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Review Articles

Vol. 12 No. 3 (2025)

Emerging trends in soil and crop sensing for enhanced data-driven decision making in precision agriculture

DOI
https://doi.org/10.14719/pst.9306
Submitted
5 May 2025
Published
21-07-2025 — Updated on 24-07-2025
Versions

Abstract

The integration of advanced soil and crop sensing technologies with data-driven strategies is revolutionising precision agriculture, addressing urgent global challenges such as increasing food demand and sustainability. Recent advancements in both proximal and remote sensing methods, including electromagnetic, optical, thermal and LiDAR systems, are enhancing the ability to assess soil status, moisture levels, nutrient availability and crop development. Moreover, the innovative application of artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT) is transforming raw sensor data into actionable insights, enabling more efficient irrigation, optimised nutrient management and improved yield prediction. These technologies are improving operational efficiency considerably by limiting the wastage of resources, lowering labour needs and allowing for timely interventions. Notably, multispectral and hyperspectral imaging are being applied for crop health monitoring, AI-driven pest detection and biomass estimation using 3D modelling advancing sustainable, data-driven precision agriculture. However, despite these promising developments, challenges remain, including difficulties in calibration, system interoperability and the high costs associated with implementation. Therefore, this review addresses the need for standardized methodologies, user-friendly tools for farmers and scalable AI solutions to enhance adoption. Ultimately, by aligning cutting-edge technology with practical agricultural needs, these innovations pave the way for more climate-resilient, productive and sustainable smart farming practices.

References

  1. 1. Sharma S. Precision agriculture: reviewing the advancements, technologies and applications in precision agriculture for improved crop productivity and resource management. Rev Food Agric. 2023;4 (2):45–9. https://doi.org/10.26480/rfna.02.2023.45.49
  2. 2. Adeleye Yusuff Adewuyi, Blessing Anyibama, Kayode Blessing Adebayo, Joseph Moses Kalinzi, Samson Ademola Adeniyi, Ifeoluwa Wada. Precision agriculture: leveraging data science for sustainable farming. Int J Sci Res Arch. 2024;12 (2):1122–9. https://doi.org/10.30574/ijsra.2024.12.2.1371
  3. 3. Alazzai WK, Abood BS, Al-Jawahry HM, Obaid MK. Precision farming: the power of AI and IoT technologies. InE3S Web of Conferences 2024 (Vol. 491, p. 04006). EDP Sciences. https://doi.org/10.1051/e3sconf/202449104006
  4. 4. Xu Y, Smirnov M, Kohler MC, Dong Z, Amineh RK, Li F, Rojas-Cessa R. A survey of wireless soil sensing technologies. IEEE Access. 2024 Jan 10;12:12010-38. https://doi.org/10.1109/ACCESS.2024.3352006
  5. 5. Singh A, Rajput VD, Varshney A, Sharma R, Ghazaryan K, Minkina T, Alexiou A, El-Ramady H. Revolutionizing crop production: nanoscale wonders-current applications, advances and future frontiers. Egyptian Journal of Soil Science. 2024 Mar 1;64 (1):221-58. https://doi.org/10.21608/ejss.2023.246354.1684
  6. 6. Laveglia S, Altieri G, Genovese F, Matera A, Di Renzo GC. Advances in sustainable crop management: integrating precision agriculture and proximal sensing. AgriEngineering. 2024 Sep 2;6 (3):3084-120. https://doi.org/10.3390/agriengineering6030177
  7. 7. Ranwa M, Sri KR, Khare A, Kumar V, Kumar K, Rajeshwar JR, Niharika M. A summative review of advances in sensor technology for precision agriculture. Archives of Current Research International.;24 (10). https://doi.org/10.9734/acri/2024/v24i10929
  8. 8. Abdul-Niby M, Farhat M, Abdullah M, Nazzal A. A low cost automated weather station for real time local measurements. Engineering, Technology & Applied Science Research. 2017 Jun 12;7 (3):1615-8. https://doi.org/10.48084/etasr.1187
  9. 9. Chen L, Xia C, Zhao Z, Fu H, Chen Y. AI-driven sensing technology. Sensors. 2024 Jan;24 (10):2958. https://doi.org/10.3390/s24102958
  10. 10. Kayad A, Paraforos DS, Marinello F, Fountas S. Latest advances in sensor applications in agriculture. Agriculture. 2020 Aug 17;10 (8):362. https://doi.org/10.3390/agriculture10080362
  11. 11. Said M, Fattahi J, Ghnimi S, Ghayoula R, Boulejfen N. Measuring electromagnetic properties of vegetal soil for wireless underground sensor networks in precision agriculture. Applied Sciences. 2024;14 (24):11884. https://doi.org/10.3390/app142411884
  12. 12. Dhamu VN, Somenahally AC, Paul A, Muthukumar S, Prasad S. Characterization of an in-situ soil organic carbon (SOC) via a smart-electrochemical sensing approach. Sensors. 2024;24 (4):1153. https://doi.org/10.3390/s24041153
  13. 13. Piccini C, Metzger K, Debaene G, Stenberg B, Götzinger S, Borůvka L, Sandén T, Bragazza L, Liebisch F. In‐field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review. European Journal of Soil Science. 2024 Mar;75 (2):e13481. https://doi.org/10.1111/ejss.13481
  14. 14. Liu TX, Zhu HH, Li Q, Wu B, Li HJ, Hu LL, Yan DM. Experimental study on progressive interfacial mechanical behaviors using fiber optic sensing cable in frozen soil. Journal of Rock Mechanics and Geotechnical Engineering. 2025;17 (3):1828-46. https://doi.org/10.1016/j.jrmge.2024.03.003
  15. 15. Shrestha MM, Wei L. Perspectives on the roles of real time nitrogen sensing and IoT integration in smart agriculture. Journal of The Electrochemical Society. 2024 Feb 28;171 (2):027526. https://doi.org/10.1149/1945-7111/ad22d8
  16. 16. Shrestha G, Calvelo-Pereira R, Poggio M, Jeyakumar P, Roudier P, Kereszturi G anderson CW. Predicting cadmium fractions in agricultural soils using proximal sensing techniques. Environmental Pollution. 2024 May 15;349:123889. https://doi.org/10.1016/j.envpol.2024.123889
  17. 17. Sakai M. Time-domain reflectometry of electronic devices. JSAP Review. 2025;2025:250403.
  18. 18. Lobach IA, Fotiadi AA, Yatseev VA, Konstantinov YA, Barkov FL, Claude D, Kambur DA, Belokrylov ME, Turov AT, Korobko DA. Newest methods and approaches to enhance the performance of optical frequency-domain reflectometers. Sensors. 2024 Aug 22;24 (16):5432. https://doi.org/10.3390/s24165432
  19. 19. Koumoulidis D, Efthimiadou A, Katsenios N, Hadjimitsis D. A review: soil properties mapping estimation using remote and proximal sensing data. InTenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024) 2024 Sep 13 (Vol. 13212, pp. 59-72). SPIE. https://doi.org/10.1117/12.3034339
  20. 20. Saeidfirozeh H, Kubelík P, Laitl V, Křivková A, Vrábel J, Rammelkamp K, Schröder S, Gornushkin IB, Képeš E, Žabka J, Ferus M. Laser-induced breakdown spectroscopy in space applications: Review and prospects. TrAC Trends in Analytical Chemistry. 2024 Oct 9:117991. https://doi.org/10.1016/j.trac.2024.117991
  21. 21. Sulthana SF, Iqbal UM, Suseela SB, Anbazhagan R, Chinthaginjala R, Chitathuru D, Ahmad I, Kim TH. Electrochemical sensors for heavy metal ion detection in aqueous medium: a systematic review. ACS omega. 2024 Jun 5;9 (24):25493-512. https://doi.org/10.1021/acsomega.4c00933
  22. 22. Mahore V, Soni P, Patidar P, Machavaram R. An IoT-enabled penetrometer with constant-rate penetration for accurate measurement of soil cone index. Cogent Engineering. 2024 Dec 31;11 (1):2372088. https://doi.org/10.1080/23311916.2024.2372088
  23. 23. Khose SB, Mailapalli DR. UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice. Geocarto international. 2024 Jan 1;39 (1):2373867. https://doi.org/10.1080/10106049.2024.2373867
  24. 24. Guebsi R, Mami S, Chokmani K. Drones in precision agriculture: a comprehensive review of applications, technologies and challenges. Drones. 2024 Nov 19;8 (11):686. https://doi.org/10.3390/drones8110686
  25. 25. Dietz K, Drechsel F. 61. Digital agriculture.
  26. 26. Shi H, Liu Z, Li S, Jin M, Tang Z, Sun T, Liu X, Li Z, Zhang F, Xiang Y. Monitoring soybean soil moisture content based on UAV multispectral and thermal-infrared remote-sensing information fusion. Plants. 2024 Aug 29;13 (17):2417. https://doi.org/10.3390/plants13172417
  27. 27. Fu Y, Zhu Z, Liu L, Zhan W, He T, Shen H, Zhao J, Liu Y, Zhang H, Liu Z, Xue Y. Remote sensing time series analysis: a review of data and applications. Journal of Remote Sensing. 2024 Dec 11;4:0285. https://doi.org/10.34133/remotesensing.0285
  28. 28. Wang J, Wang Y, Li G, Qi Z. Integration of remote sensing and machine learning for precision agriculture: a comprehensive perspective on applications. Agronomy. 2024 Sep 1;14 (9):1975. https://doi.org/10.3390/agronomy14091975
  29. 29. Wang Z, Wu W, Liu H. Spatial estimation of soil organic carbon content utilizing planetscope, sentinel-2 and sentinel-1 data. Remote Sensing. 2024 Sep 3;16 (17):3268. https://doi.org/10.3390/rs16173268
  30. 30. Adla S, Bruckmaier F, Arias-Rodriguez LF, Tripathi S, Pande S, Disse M. Impact of calibrating a low-cost capacitance-based soil moisture sensor on aquacrop model performance. Journal of Environmental Management. 2024 Feb 27;353:120248. https://doi.org/10.1016/j.jenvman.2024.120248
  31. 31. Qi Q, Yang H, Zhou Q, Han X, Jia Z, Jiang Y, Chen Z, Hou L, Mei S. Performance of soil moisture sensors at different salinity levels: comparative analysis and calibration. Sensors. 2024 Sep 29;24 (19):6323. https://doi.org/10.3390/s24196323
  32. 32. Meng X, Zeng J, Yang Y, Zhao W, Ma H, Letu H, Zhu Q, Liu Y, Wang P, Peng J. High-resolution soil moisture mapping through passive microwave remote sensing downscaling. The Innovation Geoscience. 2024 Nov 27;2 (4):100105-. https://doi.org/10.59717/j.xinn-geo.2024.100105
  33. 33. Ramachandran V, Ramalakshmi R, Kavin BP, Hussain I, Almaliki AH, Almaliki AA, Elnaggar AY, Hussein EE. Exploiting IoT and its enabled technologies for irrigation needs in agriculture. Water. 2022 Feb 24;14 (5):719. https://doi.org/10.3390/w14050719
  34. 34. Loria N, Lal R, Chandra R. Handheld In situ methods for soil organic carbon assessment. Sustainability. 2024 Jun 29;16 (13):5592. https://doi.org/10.3390/su16135592
  35. 35. Rukaitė J, Juknevičius D, Kriaučiūnienė Z, Šarauskis E. Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers and economic benefits. Journal of Agriculture and Food Research. 2024 Dec 1;18:101434. https://doi.org/10.1016/j.jafr.2024.101434
  36. 36. Rieger EK. Modeling of attitude determination and control subsystem ( ADCS) for a 6u cubesat for tracking objects in space. 2024;
  37. 37. Aranda Britez DA, Tapia Córdoba A, Johnson P, Pacheco Viana EE, Millán Gata P. Improving the calibration of low-cost sensors using data assimilation. Sensors. 2024 Dec 8;24 (23):7846. https://doi.org/10.3390/s24237846
  38. 38. Székely Á, Szalóki T, Jancsó M, Pauk J, Lantos C. Reference field spectrometric data of albino rice plants. Data in Brief. 2024 Mar 14;54:110319. https://doi.org/10.1016/j.dib.2024.110319
  39. 39. Erasmus CS, Booysen MJ, Drew D. Dataset of dendrometer and environmental parameter measurements of two different species of the group of genera known as eucalypts in South Africa and Portugal. Data in Brief. 2024 Dec 1;57:111035. https://doi.org/10.1016/j.dib.2024.111035
  40. 40. Ferreira L, Sousa JJ, Lourenço JM, Peres E, Morais R, Pádua L. Comparative analysis of tls and uav sensors for estimation of grapevine geometric parameters. Sensors. 2024 Aug 11;24 (16):5183. https://doi.org/10.3390/s24165183
  41. 41. Rühlmann J, Bönecke E, Meyer S. Die kartierung von parametern zur bestimmung der bodentextur. Insensorgestützte kartierung von bodeneigenschaften für die teilflächenspezifische kalkung: textur, pH und humus: von den messwerten zur streukarte 2024 Sep 4 (pp. 33-57). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-69174-8_3
  42. 42. Liu Y, Razman MR, Zakaria SZ, Lee KE, Khan SU, Albanyan A. Personalized context-aware systems for sustainable agriculture development using ubiquitous devices and adaptive learning. Computers in Human Behavior. 2024 Nov 1;160:108375. https://doi.org/10.1016/j.chb.2024.108375
  43. 43. Golubin AV, Kopnin AA. Prospects and opportunities for using AI technologies in the agricultural sector. InE3S Web of Conferences 2024 (Vol. 537, p. 08012). EDP Sciences. https://doi.org/10.1051/e3sconf/202453708012
  44. 44. Tripathi V, Srivastava PA. “ Soil test-based manure proposal for focused yield ” of harvests : A survey. 2020;13 (6):36–44.
  45. 45. Valverde A. Landscape of key stakeholders for the development of soil information systems. Gates Open Res. 2024 Sep 24;8 (99):99.
  46. 46. DeBruin J, Aref T, Tirado Tolosa S, Hensley R, Underwood H, McGuire M, Soman C, Nystrom G, Parkinson E, Li C, Moose SP. Breaking the field phenotyping bottleneck in maize with autonomous robots. Communications biology. 2025 Mar 21;8 (1):467.
  47. 47. Aguilera C, Barrera F, Lumbreras F, Sappa AD, Toledo R. Multispectral image feature points. Sensors (Switzerland). 2012;12 (9):12661–72. https://doi.org/10.3390/s120912661
  48. 48. Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote sensing. 2015 Apr 2;7 (4):4026-47. https://doi.org/10.3390/rs70404026
  49. 49. Ram BG, Oduor P, Igathinathane C, Howatt K, Sun X. A systematic review of hyperspectral imaging in precision agriculture: analysis of its current state and future prospects. Computers and Electronics in Agriculture. 2024 Jul 1;222:109037. https://doi.org/10.1016/j.compag.2024.109037
  50. 50. García-Vera YE, Polochè-Arango A, Mendivelso-Fajardo CA, Gutiérrez-Bernal FJ. Hyperspectral image analysis and machine learning techniques for crop disease detection and identification: A review. Sustainability. 2024 Jul 16;16 (14):6064. https://doi.org/10.3390/su16146064
  51. 51. Symeonaki E, Arvanitis K, Piromalis D. A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Applied Sciences. 2020 Jan 23;10 (3):813. https://doi.org/10.3390/app10030813
  52. 52. Dragavtsev V, Nartov VP. Application of thermal imaging in agriculture and forestry. Eur. Agrophys. J. 2015;2:15. https://doi.org/10.17830/j.eaj.2015.01.015
  53. 53. Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture—a review. Advances in remote Sensing. 2014 Sep 3;3 (3):128-40. https://doi.org/10.4236/ars.2014.33011
  54. 54. Debnath S, Paul M, Debnath T. Applications of lidar in agriculture and future research directions. Journal of Imaging. 2023 Feb 24;9 (3):57. https://doi.org/10.3390/jimaging9030057
  55. 55. Manapragada NV, Mandelmilch M, Roitberg E, Kizel F, Natanian J. Remote sensing for environmentally responsive urban built environment: a review of tools, methods and gaps. Remote Sensing Applications: Society and Environment. 2025 Mar 22:101529. https://doi.org/10.1016/j.rsase.2025.101529
  56. 56. Micheletto MJ, Chesñevar CI, Santos R. Methods and applications of 3D ground crop analysis using lidar technology: A survey. Sensors. 2023 Aug 16;23 (16):7212. https://doi.org/10.3390/s23167212
  57. 57. Mitsanis C, Hurst W, Tekinerdogan B. A 3D functional plant modelling framework for agricultural digital twins. Computers and Electronics in Agriculture. 2024 Mar 1;218:108733. https://doi.org/10.1016/j.compag.2024.108733
  58. 58. Mehmood K, Anees SA, Rehman A, Tariq A, Zubair M, Liu Q, Rabbi F, Khan KA, Luo M. Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience. Ecological Informatics. 2024 May 1;80:102532. https://doi.org/10.1016/j.ecoinf.2024.102532
  59. 59. Priya RS, Rahamathunnisa U. A comprehensive study of remote sensing technology for agriculture crop monitoring. Nature Environment & Pollution Technology. 2024 Jun 1;23 (2). https://doi.org/10.46488/NEPT.2024.v23i02.035
  60. 60. Moustaka J, Moustakas M. Early-stage detection of biotic and abiotic stress on plants by chlorophyll fluorescence imaging analysis. Biosensors. 2023 Aug 8;13 (8):796. https://doi.org/10.3390/bios13080796
  61. 61. Linn AI, Zeller AK, Pfündel EE, Gerhards R. Features and applications of a field imaging chlorophyll fluorometer to measure stress in agricultural plants. Precision Agriculture. 2021 Jun;22 (3):947-63. https://doi.org/10.1007/s11119-020-09767-7
  62. 62. Zhu H, Lin C, Liu G, Wang D, Qin S, Li A, Xu JL, He Y. Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection. Frontiers in Plant Science. 2024 Oct 24;15:1435016. https://doi.org/10.3389/fpls.2024.1435016
  63. 63. Walsh JJ, Mangina E, Negrão S. Advancements in imaging sensors and AI for plant stress detection: a systematic literature review. Plant Phenomics. 2024 Mar 1;6:0153. https://doi.org/10.34133/plantphenomics.0153
  64. 64. Zanin AR, Neves DC, Teodoro LP, da Silva Júnior CA, da Silva SP, Teodoro PE, Baio FH. Reduction of pesticide application via real-time precision spraying. Scientific reports. 2022 Apr 4;12 (1):5638. https://doi.org/10.1038/s41598-022-09607-w
  65. 65. Shammi SA, Huang Y, Feng G, Tewolde H, Zhang X, Jenkins J, Shankle M. Application of UAV multispectral imaging to monitor soybean growth with yield prediction through machine learning. Agronomy. 2024 Mar 26;14 (4):672. https://doi.org/10.3390/agronomy14040672
  66. 66. El-Kenawy ES, Alhussan AA, Khodadadi N, Mirjalili S, Eid MM. Predicting potato crop yield with machine learning and deep learning for sustainable agriculture. Potato Research. 2024 Jul 13:1-34. https://doi.org/10.1007/s11540-024-09753-w
  67. 67. Yuan Q, Shafri HZ, Alias AH, Hashim SJ. Multiscale semantic feature optimization and fusion network for building extraction using high-resolution aerial images and lidar data. Remote Sensing. 2021 Jun 24;13 (13):2473. https://doi.org/10.3390/rs13132473
  68. 68. Hosseini M, McNairn H, Mitchell S, Robertson LD, Davidson A, Ahmadian N, Bhattacharya A, Borg E, Conrad C, Dabrowska-Zielinska K, De Abelleyra D. A comparison between support vector machine and water cloud model for estimating crop leaf area index. Remote Sensing. 2021 Apr 1;13 (7):1348. https://doi.org/10.3390/rs13071348
  69. 69. Cheng M, Jiao X, Shi L, Penuelas J, Kumar L, Nie C, Wu T, Liu K, Wu W, Jin X. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing. Scientific data. 2022 Oct 21;9 (1):641. https://doi.org/10.1038/s41597-022-01761-0
  70. 70. Bushra AA, Yi G. Comparative analysis review of pioneering dbscan and successive density-based clustering algorithms. IEEE Access. 2021;9:87918–35. https://doi.org/10.1109/ACCESS.2021.3089036
  71. 71. Abekoon T, Sajindra H, Rathnayake N, Ekanayake IU, Jayakody A, Rathnayake U. A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P and K nutrient content in cabbage cultivation. Smart Agricultural Technology. 2025 Aug 1;11:100879. https://doi.org/10.1016/j.atech.2025.100879
  72. 72. Jorvekar PP, Wagh SK, Prasad JR. Predictive modeling of crop yields: A comparative analysis of regression techniques for agricultural yield prediction. Agricultural Engineering International: CIGR Journal. 2024 Sep 5;26 (2).
  73. 73. Dorwardl A, Chirwa E. The Malawi agricultural input subsidy programme: 2005/06 to 2008/09. International Journal of Agricultural Sustainability. 2011 Feb 1;9 (1). https://doi.org/10.3763/ijas.2010.0567
  74. 74. Shahab H, Naeem M, Iqbal M, Aqeel M, Ullah SS. IoT-driven smart agricultural technology for real-time soil and crop optimization. Smart Agricultural Technology. 2025 Mar 1;10:100847. https://doi.org/10.1016/j.atech.2025.100847
  75. 75. Rahman Z ur, Asaari MSM, Ibrahim H, Abidin ISZ, Ishak MK. Generative adversarial networks (GANs) for image augmentation in farming: a review. IEEE Access. 2024;12 (December):179912–43. https://doi.org/10.1109/ACCESS.2024.3505989
  76. 76. Slimani H, Mhamdi JE, Jilbab A. Deep learning structure for real-time crop monitoring based on neural architecture search and UAV. Brazilian Archives of Biology and Technology. 2024 Sep 30;67:e24231141. https://doi.org/10.1590/1678-4324-2024231141
  77. 77. Sambado S, Sparkman A, Swei A, MacDonald AJ, Young HS, Salomon J, Crews A, Ring K, Copeland S, Briggs CJ. Climate-driven variation in the phenology of juvenile Ixodes pacificus on lizard hosts. Parasites & Vectors. 2025 Apr 15;18:141. https://doi.org/10.1186/s13071-025-06749-4
  78. 78. Chen Y, Shi W, Aihemaitijiang G, Zhang F, Zhang J, Zhang Y, Pan D, Li J. Hyperspectral inversion of heavy metal content in farmland soil under conservation tillage of black soils. Scientific Reports. 2025 Jan 2;15 (1):354. https://doi.org/10.1038/s41598-024-83479-0
  79. 79. Mohanty LK, Singh NK, Raj P, Prakash A, Tiwari AK, Singh V, Sachan P. Nurturing crops, enhancing soil health and sustaining agricultural prosperity worldwide through agronomy. Journal of Experimental Agriculture International. 2024;46 (2):46-67. https://doi.org/10.9734/jeai/2024/v46i22308
  80. 80. Khan A. Soil health and fertility: modern approaches to enhancing soil quality. Frontiers in Agriculture. 2024;1(2):283-324.
  81. 81. Farmaha BS, Sekaran U, Franzluebbers AJ. Cover cropping and conservation tillage improve soil health in the southeastern United States. Agronomy Journal. 2022;114(1):296-316. https://doi.org/10.1002/agj2.20865
  82. 82. Angon PB, Anjum N, Akter MM, KC S, Suma RP, Jannat S. An overview of the impact of tillage and cropping systems on soil health in agricultural practices. Advances in Agriculture. 2023;2023(1):8861216. https://doi.org/10.1155/2023/8861216
  83. 83. Trimarco T, Harmel RD, Wardle E, Buchanan C, Brown A, Deleon E, et al. Connecting the soil health–water quality nexus under surface‐irrigated conservation tillage. 2024. https://doi.org/10.1002/jeq2.70013
  84. 84. Mamatha B, Mudigiri C, Ramesh G, Saidulu P, Meenakshi N, Prasanna CL. Enhancing soil health and fertility management for sustainable agriculture: a review. Asian J Soil Sci Plant Nutr. 2024;10:182-90. https://doi.org/10.9734/ajsspn/2024/v10i3330
  85. 85. Zhang X, Feng G, Sun XR. Advanced technologies of soil moisture monitoring in precision agriculture. Journal of Agriculture and Food Research. 2024;101473. https://doi.org/10.1016/j.jafr.2024.101473
  86. 86. Boiarskii B, Hasegawa H. Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. J Mech Contin Math Sci. 2019;4:20-9. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
  87. 87. Yu FH, Bai JC, Jin ZY, Guo ZH, Yang JX, Chen CL. Combining the critical nitrogen concentration and machine learning algorithms to estimate nitrogen deficiency in Oryza sativa from UAV hyperspectral data. J Integr Agric. 2023;22(4):1216-29. https://doi.org/10.1016/j.jia.2022.12.007
  88. 88. Swathy R, Geethalakshmi V, Pazhanivelan S, Kannan P, Annamalai S, Hwang S. Real-time nitrogen monitoring and management to augment N use efficiency and ecosystem sustainability–a review. J Hazard Mater Adv. 2024;100466. https://doi.org/10.1016/j.hazadv.2024.100466
  89. 89. Yu J, Yin X, Raper TB, Jagadamma S, Chi D. Nitrogen consumption and productivity of Gossypium hirsutum under sensor‐based variable‐rate nitrogen fertilization. Agron J. 2019;111(6):3320-8. https://doi.org/10.2134/agronj2019.03.0197
  90. 90. Wu D, Cao L, Zhou P, Li N, Li Y, Wang D. Infrared small-target detection based on radiation characteristics with a multimodal feature fusion network. Remote Sens. 2022;14(15):3570. https://doi.org/10.3390/rs14153570
  91. 91. Feng X, Bi S, Li H, Qi Y, Chen S, Shao L. Soil moisture forecasting for precision irrigation management using real-time electricity consumption records. Agric Water Manag. 2024;291:108656. https://doi.org/10.1016/j.agwat.2023.108656
  92. 92. Bwambale E, Abagale FK, Anornu GK. Data-driven model predictive control for precision irrigation management. Smart Agric Technol. 2023;3:100074. https://doi.org/10.1016/j.atech.2022.100074
  93. 93. Anjum MN, Cheema MJ, Hussain F, Wu RS. Precision irrigation: challenges and opportunities. Precis Agric. 2023:85-101. https://doi.org/10.1016/B978-0-443-18953-1.00007-6
  94. 94. Velmurugan S. An IoT based smart irrigation system using soil moisture and weather prediction.
  95. 95. Elsayed S, Rischbeck P, Schmidhalter U. Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed Hordeum vulgare cultivars. Field Crops Res. 2015;177:148-60. https://doi.org/10.1016/j.fcr.2015.03.010 Abioye EA, Hensel O, Esau TJ, Elijah O, Abidin MS, Ayobami AS, et al. Precision irrigation management using machine learning and digital farming solutions. AgriEngineering. 2022;4(1):70-103. https://doi.org/10.3390/agriengineering4010006
  96. 96. Abioye EA, Hensel O, Esau TJ, Elijah O, Abidin MS, Ayobami AS, et al. Precision irrigation management using machine learning and digital farming solutions. AgriEngineering. 2022;4(1):70-103. https://doi.org/10.3390/agriengineering4010006
  97. 97. Elshaikh A, Elsheikh E, Mabrouki J. Applications of artificial intelligence in precision irrigation. J Environ Earth Sci. 2024;6(2). https://doi.org/10.30564/jees.v6i2.6679
  98. 98. Devi KA, Priya R. Plant disease identification using the unmanned aerial vehicle images. Turk J Comput Math Educ. 2021;12(10):2396-9.
  99. 99. Nyéki A, Neményi M. Crop yield prediction in precision agriculture. Agronomy. 2022;12(10):2460. https://doi.org/10.3390/agronomy12102460
  100. 100. Shi H, Li Z, Xiang Y, Tang Z, Sun T, Du R, et al. Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): a new perspective from the temperature-vegetation index feature space. Agric Water Manag. 2024;305:109129. https://doi.org/10.1016/j.agwat.2024.109129
  101. 101. Trentin C, Ampatzidis Y, Lacerda C, Shiratsuchi L. Tree crop yield estimation and prediction using remote sensing and machine learning: a systematic review. Smart Agric Technol. 2024:100556. https://doi.org/10.1016/j.atech.2024.100556
  102. 102. Wasay A, Ahmed Z, Abid AU, Sarwar A, Ali A. Optimizing crop yield through precision agronomy techniques. Trends Biotechnol Plant Sci. 2024;2(1):25-35. https://doi.org/10.62460/TBPS/2024.014
  103. 103. Boahen JO, Choudhary M. Advancements in precision agriculture: integrating computer vision for intelligent soil and crop monitoring in the era of artificial intelligence. Int J Sci Res Eng Manag. 2024;8(3):1-5. https://doi.org/10.55041/IJSREM29725
  104. 104. Gómez-Astorga MJ, Villagra-Mendoza K, Masís-Meléndez F, Ruíz-Barquero A, Rimolo-Donadio R. Calibration of low-cost moisture sensors in a biochar-amended sandy loam soil with different salinity levels. Sensors. 2024;24(18):5958. https://doi.org/10.3390/s24185958
  105. 105. Ben-Shoushan R, Brook A. Fused thermal and RGB imagery for robust detection and classification of dynamic objects in mixed datasets via pre-trained high-level CNN. Remote Sens. 2023;15(3):723. https://doi.org/10.3390/rs15030723
  106. 106. Hundal GS, Laux CM, Buckmaster D, Sutton MJ, Langemeier M. Exploring barriers to the adoption of internet of things-based precision agriculture practices. Agriculture. 2023;13(1):163. https://doi.org/10.3390/agriculture13010163
  107. 107. Musa P, Sugeru H, Wibowo EP. Wireless sensor networks for precision agriculture: a review of NPK sensor implementations. Sensors. 2023;24(1):51. https://doi.org/10.3390/s24010051
  108. 108. Rajak P, Ganguly A, Adhikary S, Bhattacharya S. Internet of things and smart sensors in agriculture: scopes and challenges. J Agric Food Res. 2023;14:100776. https://doi.org/10.1016/j.jafr.2023.100776
  109. 109. San Emeterio de la Parte M, Martínez-Ortega JF, Hernández Díaz V, Martínez NL. Big data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability. J Big Data. 2023;10(1):52. https://doi.org/10.1186/s40537-023-00729-0
  110. 110. Sitokonstantinou V, Porras ED, Bautista JC, Piles M, Athanasiadis I, Kerner H, et al. Causal machine learning for sustainable agroecosystems. arXiv. 2024;arXiv:2408.13155.
  111. 111. Kroupová ZŽ, Aulová R, Rumánková L, Bajan B, Čechura L, Šimek P, et al. Drivers and barriers to precision agriculture technology and digitalisation adoption: meta-analysis of decision choice models. Precis Agric. 2025;26(1):17. https://doi.org/10.1007/s11119-024-10213-1

Downloads

Download data is not yet available.