Skip to main navigation menu Skip to main content Skip to site footer

Review Articles

Early Access

Smart water management in agroecosystems: innovations and challenges in a changing climate- A review

DOI
https://doi.org/10.14719/pst.6601
Submitted
9 December 2024
Published
14-06-2025
Versions

Abstract

Over the decade, rapid climatic changes threaten agroecosystem stability and food security. The rapid transition from natural vegetation to agricultural land results to alteration of surface energy balance. Numerous interactions occur within the agroecosystem among its diverse components. Properly understanding these interactions helps mitigate environmental impact through modern climate-smart technologies and sustainable crop water management based on unique needs. Smart water management paves the pathway, particularly in water crises phase through the use of numerous contemporary artificial intelligences, machine learning tools, agrometeorological models and Internet of Things based modern watering devices aid in the efficient use of resources both on and off farm to improve agricultural output and quality. The study's objective is reviewing core technologies including advanced sensors, internet of things, remote sensing and agrometeorological models. The highlights of this study to investigate SWM systems. A comparative analysis of existing technologies identifies challenges such as high cost, data privacy concerns and policy gap. To address these gaps, this study proposes an integrated approach that combines artificial intelligence, remote sensing and IoT framework as most effective approach, enabling real time monitoring, precise irrigation scheduling and adaptive response to climate variability. Advancing these technologies with suitable, cost effective solutions, and policy interventions is crucial for ensuring climate resilient, increasing the efficiency of the smart management system and sustainable agricultural water management.

References

  1. 1. Belhassan K. Water scarcity management. In: Smith J, Brown K, editors. Water safety, security and sustainability: Threat detection and mitigation. New York: Springer; 2021. p. 443–62. https://doi.org/10.1007/978-3-030-76008-3_19
  2. 2. Akhtar N, Syakir Ishak MI, Bhawani SA, Umar K. Various natural and anthropogenic factors responsible for water quality degradation: A review. Water. 2021;13(19):2660. https://doi.org/10.3390/w13192660
  3. 3. Kumar S, Yadav A, Kumar A, Hasanain M, Shankar K, Karan S, et al. Climate-smart irrigation practices for improving water productivity in India: a comprehensive review. Int J Environ Clim Change. 2023;13(12):333–48. https://doi.org/10.9734/ijecc/2023/v13i123689
  4. 4. Ishfaq M, Farooq M, Zulfiqar U, Hussain S, Akbar N, Nawaz A, et al. Alternate wetting and drying: A water-saving and eco-friendly rice production system. Agric Water Manag. 2020;241:106363. https://doi.org/10.1016/j.agwat.2020.106363
  5. 5. Kumar KA, Rajitha G. Alternate wetting and drying (AWD) irrigation—a smart water-saving technology for rice: a review. Int J Curr Microbiol Appl Sci. 2019;8(3):2561–71. https://doi.org/10.20546/ijcmas.2019.803.304
  6. 6. Davydenko L, Davydenko N, Deja A, Wiśnicki B, Dzhuguryan T. Efficient energy management for the smart sustainable city multifloor manufacturing clusters: a formalization of the water supply system operation conditions based on monitoring water consumption profiles. Energies. 2023;16(11):4519. https://doi.org/10.3390/en16114519
  7. 7. Li J, Yang X, Sitzenfrei R. Rethinking the framework of smart water system: a review. Water. 2020;12(2):412. https://doi.org/10.3390/w12020412
  8. 8. Boretti A, Rosa L. Reassessing the projections of the World Water Development Report. NPJ Clean Water. 2019;2(1):15. https://doi.org/10.1038/s41545-019-0039-9
  9. 9. Aivazidou E, Banias G, Lampridi M, Vasileiadis G, Anagnostis A, Papageorgiou E, et al. Smart technologies for sustainable water management: an urban analysis. Sustainability. 2021;13(24):13940. https://doi.org/10.3390/su132413940
  10. 10. Gupta AD, Pandey P, Feijóo A, Yaseen ZM, Bokde ND. Smart water technology for efficient water resource management: a review. Energies. 2020;13(23):6268. https://doi.org/10.3390/en13236268
  11. 11. Durodola OS. The impact of climate change-induced extreme events on agriculture and food security: a review of Nigeria. Agric Sci. 2019;10(4):538–54. https://doi.org/10.4236/as.2019.104038
  12. 12. Dasgupta S, Robinson EJ. Attributing changes in food insecurity to a changing climate. Sci Rep. 2022;12(1):4709. https://doi.org/10.1038/s41598-022-08696-x
  13. 13. Hussain S, Aslam MU, Javed M, Zahra M, Ejaz H, Mushtaq I. Impact of climatic changes and global warming on water availability. Anthropog Pollut. 2021;5(2).
  14. 14. Konapala G, Mishra AK, Wada Y, Mann ME. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat Commun. 2020 ;11(1):3044. https://doi.org/10.1038/s41467-020-16757-w
  15. 15. Rajabalinejad A, Nozari N, Badr BR. The effect of climate change on agricultural production in Iran. Braz J Biol. 2024;83:e277383. https://doi.org/10.1590/1519-6984.277383
  16. 16. Hsieh YL, Yeh SC. The trends of major issues connecting climate change and the Sustainable Development Goals. Discov Sustain. 2024;5(1):31. https://doi.org/10.1007/s43621-024-00183-9
  17. 17. Scarano A, Olivieri F, Gerardi C, Liso M, Chiesa M, Chieppa M, et al. Selection of tomato landraces with high fruit yield and nutritional quality under elevated temperatures. J Sci Food Agric. 2020;100(6):2791–9. https://doi.org/10.1002/jsfa.10312
  18. 18. Perfecto I, Hajian-Forooshani Z, Iverson A, Irizarry AD, Lugo-Perez J, Medina N, et al. Response of coffee farms to Hurricane Maria: resistance and resilience from an extreme climatic event. Sci Rep. 2019;9(1):15668. https://doi.org/10.1038/s41598-019-51416-1
  19. 19. O’Donnell E, Kennedy M, Garrick D, Horne A, Woods R. Cultural water and Indigenous water science. Science. 2023;381(6658):619–21. https://doi.org/10.1126/science.adi0658
  20. 20. Ballestero A. The anthropology of water. Annu Rev Anthropol. 2019;48(1):405–21. https://doi.org/10.1146/annurev-anthro-102218-011428
  21. 21. Balaska V, Adamidou Z, Vryzas Z, Gasteratos A. Sustainable crop protection via robotics and artificial intelligence solutions. Machines. 2023;11(8):774. https://doi.org/10.3390/machines11080774
  22. 22. Naz N, Hameed W, Tabbassum R, Farzand A, Asif A, Mushtaq N, et al. Impact of global climate change on agricultural productivity. Int J Glob Sci. 2022;4:1–11.
  23. 23. Semeraro T, Scarano A, Leggieri A, Calisi A, De Caroli M. Impact of climate change on agroecosystems and potential adaptation strategies. Land. 2023;12(6):1117. https://doi.org/10.3390/land12061117
  24. 24. Makropoulos C, Savić DA. Urban hydroinformatics: past, present and future. Water. 2019;11:1959. https://doi.org/10.3390/w11091959
  25. 25. Rahim MS, Nguyen KA, Stewart RA, Giurco D, Blumenstein M. Machine learning and data analytic techniques in digital water metering: a review. Water. 2020;12:294. https://doi.org/10.3390/w12010294
  26. 26. Nasser N, Khan N, Karim L, ElAttar M, Saleh K. An efficient time-sensitive data scheduling approach for wireless sensor networks in smart cities. Comput Commun. 2021;175:112–22. https://doi.org/10.1016/j.comcom.2021.05.016
  27. 27. Balyan S, Jangir H, Tripathi SN, Tripathi A, Jhang T, Pandey P. Seeding a sustainable future: navigating the digital horizon of smart agriculture. Sustainability. 2024;16(2):475. https://doi.org/10.3390/su16020475
  28. 28. Hrustek L. Sustainability driven by agriculture through digital transformation. Sustainability. 2020;12(20):8596. https://doi.org/10.3390/su12208596
  29. 29. Waqas MA, Wang X, Zafar SA, Noor MA, Hussain HA, Azher Nawaz M, et al. Thermal stresses in maize: effects and management strategies. Plants. 2021;10(2):293. https://doi.org/10.3390/plants10020293
  30. 30. Reddy VS, Harivardhagini S, Sreelakshmi G. IoT and cloud-based sustainable smart irrigation system. E3S Web Conf. 2024;472:01026. https://doi.org/10.1051/e3sconf/202447201026
  31. 31. Nautiyal CT, Nautiyal P, Papnai G, Mittal H, Agrawal K, Nandini R. Importance of smart agriculture and use of artificial intelligence in shaping the future of agriculture. J Sci Res Rep. 2024;30(3):129–38. https://doi.org/10.9734/jsrr/2024/v30i31864
  32. 32. Feng Y, Ovalle M, Seale JS, Lee CK, Kim DJ, Astumian RD, et al. Molecular pumps and motors. J Am Chem Soc. 2021;143(15):5569–91. https://doi.org/10.1021/jacs.0c13240
  33. 33. Liu S, Shi X, Wong KT, Chen MT, Ye W, Zhang H, et al. Synchronous millennial surface-stratified events with AMOC and tropical dynamic changes in the northeastern Indian Ocean over the past 42 ka. Quat Sci Rev. 2022;284:107495. https://doi.org/10.1016/j.quascirev.2022.107495
  34. 34. Winstanley-Chesters R. Sustainable water management in North Korean cities. In: Pursuing sustainable urban development in North Korea. Routledge; 2025. p. 148–61. https://doi.org/10.4324/9781003372035-15
  35. 35. Patle GT, Kumar M, Khanna M. Climate-smart water technologies for sustainable agriculture: a review. J Water Clim Change. 2020;11(4):1455–66. https://doi.org/10.2166/wcc.2019.257
  36. 36. Hussain HA, Hussain S, Khaliq A, Ashraf U, Anjum SA, Men S, et al. Chilling and drought stresses in crop plants: implications, cross-talk and potential management opportunities. Front Plant Sci. 2018;9:393. https://doi.org/10.3389/fpls.2018.00393
  37. 37. Yang L, Driscoll J, Sarigai S, Wu Q, Lippitt CD, Morgan M. Towards synoptic water monitoring systems: a review of AI methods for automating water body detection and water quality monitoring using remote sensing. Sensors. 2022;22(6):2416. https://doi.org/10.3390/s22062416
  38. 38. Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: a review. Sensors. 2018;18(8):2674. https://doi.org/10.3390/s18082674
  39. 39. Kerry R, Ingram B, Hammond K, Shumate SR, Gunther D, Jensen RR, et al. Spatial analysis of soil moisture and turfgrass health to determine zones for spatially variable irrigation management. Agronoy. 2023;13(5):1267. https://doi.org/10.3390/agronomy13051267
  40. 40. Blin N, Suárez F. Evaluating the contribution of satellite-derived evapotranspiration in the calibration of numerical groundwater models in remote zones using the EEFlux tool. Sci Total Environ. 2023;858:159764. https://doi.org/10.1016/j.scitotenv.2022.159764
  41. 41. Huang W, Chen S, Yang X, Johnson E. Assessment of chlorophyll-a variations in high- and low-flow seasons in Apalachicola Bay by MODIS 250-m remote sensing. Environ Monit Assess. 2014;186:8329–42. https://doi.org/10.1007/s10661-014-4007-z
  42. 42. Junqueira AM, Mao F, Mendes TS, Simões SJ, Balestieri JA, Hannah DM. Estimation of river flow using CubeSats remote sensing. Sci Total Environ. 2021;788:147762. https://doi.org/10.1016/j.scitotenv.2021.147762
  43. 43. Roy A, Murtugudde R, Narvekar P, Sahai AK, Ghosh S. Remote sensing and climate services improve irrigation water management at the farm scale in Western-Central India. Sci Total Environ. 2023;879:163003. https://doi.org/10.1016/j.scitotenv.2023.163003
  44. 44. Jakovljevic G, Álvarez-Taboada F, Govedarica M. Long-term monitoring of inland water quality parameters using Landsat time-series and back-propagated ANN: assessment and usability in a real-case scenario. Remote Sens. 2023;16(1):68. https://doi.org/10.3390/rs16010068
  45. 45. Rolim SB, Veettil BK, Vieiro AP, Kessler AB, Gonzatti C. Remote sensing for mapping algal blooms in freshwater lakes: a review. Environ Sci Pollut Res Int. 2023;30(8):19602–16. https://doi.org/10.1007/s11356-023-25230-2
  46. 46. Chukwuma U, Gebremedhin KG, Uyeh DD. Imagining AI-driven decision making for managing farming in developing and emerging economies. Comput Electron Agric. 2024;221:108946. https://doi.org/10.1016/j.compag.2024.108946
  47. 47. Thilagavathi N, Subramani T, Suresh M, Karunanidhi D. Mapping of groundwater potential zones in Salem Chalk Hills, Tamil Nadu, India, using remote sensing and GIS techniques. Environ Monit Assess. 2015;187:1–7. https://doi.org/10.1007/s10661-015-4376-y
  48. 48. Obasi SN, Aa TV, Obasi CC, Jokthan GE, Adjei EA, Keyagha ER. Harnessing artificial intelligence for sustainable agriculture: a comprehensive review of African applications in spatial analysis and precision agriculture. Big Data Agri. 2024;6(1):1–13. https://doi.org/10.26480/bda.01.2024.01.13
  49. 49. Lazarovitch N, Kisekka I, Oker TE, Brunetti G, Wöhling T, Xianyue L, et al. Modelling of irrigation and related processes with HYDRUS. Adv Agron. 2023;181:79–181. https://doi.org/10.1016/bs.agron.2023.05.002
  50. 50. Shashikant V, Mohamed Shariff AR, Wayayok A, Kamal MR, Lee YP, Takeuchi W. Utilizing TVDI and NDWI to classify severity of agricultural drought in Chuping, Malaysia. Agronom. 2021;11(6):1243. https://doi.org/10.3390/agronomy11061243
  51. 51. Flynn KD, Wyatt BM, McInnes KJ. Novel cosmic ray neutron sensor accurately captures field-scale soil moisture trends under heterogeneous soil textures. Water. 2021;13(21):3038. https://doi.org/10.3390/w13213038
  52. 52. Schirmbeck LW, Fontana DC, Schirmbeck J, Dalmago GA, Fernandes JM. Water monitoring of soybean crops using the TVDI obtained from surface radiometric sensors. Pesq. Agropec Bras. 2022;57:e02581. https://doi.org/10.1590/S1678-3921.pab2022.v57.02581
  53. 53. Li DW, Xu CY, Song JC, Tian MQ, Xing XJ. Development of a remote intelligent irrigation control system based on IoT. Water Sav Irrig. 2017;10:87–91.
  54. 54. Ullah A, Zubair M, Zulfiqar MH, Kamsong W, Karuwan C, Massoud Y, et al. Highly sensitive screen-printed soil moisture sensor array as green solutions for sustainable precision agriculture. Sens Actuators A Phys. 2024;371:115297. https://doi.org/10.1016/j.sna.2024.115297
  55. 55. Meier J, Zabel F, Mauser W. A global approach to estimate irrigated areas–a comparison between different data and statistics. Hydrol Earth Syst Sci. 2018;22(2):1119–33. https://doi.org/10.5194/hess-22-1119-2018
  56. 56. Cui K, Huang Y, Li X, Li J, Lu X, Chui TC. PSDNet: Plant status detection network utilized in an intelligent Bougainvillaea glabra sensing and watering system. IEEE Sens J. 2024;24(11):18685–98. https://doi.org/10.1109/JSEN.2024.3390681
  57. 57. Galli A, Peruzzi C, Gangi F, Masseroni D. ArduHydro: a low-cost device for water level measurement and monitoring. J Agric Eng. 2024;55(1). https://doi.org/10.4081/jae.2024.1554
  58. 58. Thote D, Lanjewar V, Sharma V, Agrawal P, Soni VK. IoT and machine learning-based smart soil irrigation farming systems. In: 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). 2024. https://doi.org/10.1109/SCEECS61402.2024.10482246
  59. 59. Egerer M, Ristolainen A, Piho L, Vihman L, Kruusmaa M. Hall effect sensor-based low-cost flow monitoring device: design and validation. IEEE Sens J. 2024. https://doi.org/10.1109/JSEN.2024.3354194
  60. 60. Borges RC, Beuter CH, Dourado VC, Bento ME. Internet of Things application in an automated irrigation prototype powered by photovoltaic energy. Energies. 2024;17(9):2219. https://doi.org/10.3390/en17092219
  61. 61. Machado MR, Júnior TR, Silva MR, Martins JB. Smart water management system using the microcontroller ZR16S08 as IoT solution. In: 2019 IEEE 10th Latin Am Symp Circuits Syst (LASCAS); 2019 Feb 24. p. 169-72. IEEE. https://doi.org/10.1109/LASCAS.2019.8667571
  62. 62. Campos GS, Rocha AR, Gondim R, Coelho da Silva TL, Gomes DG. Smart & green: An internet-of-things framework for smart irrigation. Sensors. 2020;20(1):190. https://doi.org/10.3390/s20010190
  63. 63. Ayoub I, Balakrichenan S, Khawam K, Ampeau B. DNS for IoT: a survey. Sensors. 2023;23(9):4473. https://doi.org/10.3390/s23094473
  64. 64. Krishnan RS, Julie EG, Robinson YH, Raja S, Kumar R, Thong PH. Fuzzy logic-based smart irrigation system using Internet of Things. J Clean Prod. 2020;252:119902. https://doi.org/10.3390/su142013384
  65. 65. Samarinas N, Tsakiridis NL, Kalopesa E, Zalidis GC. Soil loss estimation by water erosion in agricultural areas, introducing artificial intelligence geospatial layers into the RUSLE model. Land. 2024;13(2):174. https://doi.org/10.3390/land13020174
  66. 66. Kumar P, Udayakumar A, Anbarasa Kumar A, Senthamarai Kannan K, Krishnan N. Multiparameter optimization system with DCNN in precision agriculture for advanced irrigation planning and scheduling based on soil moisture estimation. Environ Monit Assess. 2023;195(1):13. https://doi.org/10.1007/s10661-022-10529-3
  67. 67. Sagar A, Hasan M, Singh DK, Al-Ansari N, Chakraborty D, Singh MC, et al. Development of a smart weighing lysimeter for measuring evapotranspiration and developing crop coefficient for greenhouse Chrysanthemum. Sensors. 2022;22(16):6239. https://doi.org/10.3390/s22166239
  68. 68. Junior AA, da Silva TJ, Andrade SP. Smart IoT lysimetry system by weighing with automatic cloud data storage. Smart Agric Technol. 2023;4:100177. https://doi.org/10.1016/j.atech.2023.100177
  69. 69. Gupta A, Singh R, Kumar M. Design, development and performance evaluation of Iot-enabled digital weighing-type field lysimeter. [preprint] SSRN. https://doi.org/10.2139/ssrn.4824659
  70. 70. Castañeda-Miranda A, Castaño-Meneses VM. Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods. Measurement. 2020;164:108043. https://doi.org/10.1016/j.measurement.2020.108043
  71. 71. Valente A, Costa C, Pereira L, Soares B, Lima J, Soares S. A LoRaWAN IoT system for smart agriculture for vine water status determination. Agriculture. 2022;12(10):1695. https://doi.org/10.3390/agriculture12101695
  72. 72. Benzaouia M, Hajji B, Mellit A, Rabhi A. Fuzzy-IoT smart irrigation system for precision scheduling and monitoring. Comput Electron Agric. 2023;215:108407. https://doi.org/10.1016/j.compag.2023.108407
  73. 73. Zhu S, Lin F. Intelligent agricultural water and fertilizer irrigation system based on ZigBee technology and STM32. In: Proceedings of the 2023 12th International Conference on Networking and Communication Computing; 2023 Dec 15. p. 144–8. https://doi.org/10.1145/3638837.3638859
  74. 74. Pineda-Castro D, Diaz H, Soto J, Urban MO. Lysiphen: a gravimetric IoT device for near real-time high-frequency crop phenotyping: a case study on common beans. Plant Methods. 2024;20(1):39. https://doi.org/10.1186/s13007-024-01170-x
  75. 75. Jani KA, Chaubey NK. SMAIoT-ferti: a smart cropland monitoring and optimal fertigation IoT system. Int J Inf Technol. 2024;16(4):2253–61. https://doi.org/10.1007/s41870-024-01731-2
  76. 76. Kumar GK, Bangare ML, Bangare PM, Kumar CR, Raj R, Arias-Gonzáles JL, et al. Internet of Things sensors and support vector machine integrated intelligent irrigation system for the agriculture industry. Discov Sustain. 2024;5(1):6. https://doi.org/10.1007/s43621-024-00179-5
  77. 77. Vatari S, Bakshi A, Thakur T. Greenhouse by using IoT and cloud computing. In: 2016 IEEE Int Conf Recent Trends Electron Inf Commun Technol (RTEICT); 2016 May 20. p. 246–50. IEEE. https://doi.org/10.1109/RTEICT.2016.7807821
  78. 78. Hung P, Peng K. Green energy water-autonomous greenhouse system: an alternative technology approach toward sustainable smart–green vertical greening in a smart city. In: Green City Planning and Practices in Asian Cities: Sustainable Development and Smart Growth in Urban Environments. 2018. p. 315–35. https://doi.org/10.1007/978-3-319-70025-0_16
  79. 79. Saha A, Das PS, Banik BC. Smart greenhouse for controlling & monitoring temperature, soil & humidity using IoT. In: 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP); 2022 Feb 12; Visakhapatnam, India. p. 1–4. https://doi.org/10.1109/AISP53593.2022.9760541
  80. 80. Soheli SJ, Jahan N, Hossain MB, Adhikary A, Khan AR, Wahiduzzaman M. Smart greenhouse monitoring system using internet of things and artificial intelligence. Wirel Pers Commun. 2022;124(4):3603–34. https://doi.org/10.1007/s11277-022-09528-x
  81. 81. Adiga A, Chandra Darshan J, Umesh KK. Smart greenhouse management system using IoT and multivariate fuzzy logic. In: Hassanien AE, Anand S, Jaiswal A, Kumar P, editors. Innovative Computing and Communications. ICICC 2024. Lecture Notes in Networks and Systems, vol 1043. Singapore: Springer; 2024. p. 261–71. https://doi.org/10.1007/978-981-97-4228-8_18
  82. 82. Rahman H, Shah UM, Riaz SM, Kifayat K, Moqurrab SA, Yoo J. Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning. Future Gener Comput Syst. 2024;156:285–300. https://doi.org/10.1016/j.future.2024.03.023
  83. 83. Shekarian SM, Aminian M, Fallah AM, Moghaddam VA. AI-powered sensor fault detection for cost-effective smart greenhouses. Comput Electron Agric. 2024;224:109198. https://doi.org/10.1016/j.compag.2024.109198
  84. 84. Palmer PL. AgriMet: A reclamation tool for irrigation water management. In: World Environ Water Resour Congr 2011: Bearing Knowledge for Sustainability. 2011;2682–91. https://doi.org/10.1061/41173(414)279
  85. 85. Yang Y, Guan H, Batelaan O, McVicar TR, Long D, Piao S, et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci Rep. 2016;6(1):23284. https://doi.org/10.1038/srep23284
  86. 86. Gupta D, Gujre N, Singha S, Mitra S. Role of existing and emerging technologies in advancing climate-smart agriculture through modelling: A review. Ecol Inform. 2022;71:101805. https://doi.org/10.1016/j.ecoinf.2022.101805
  87. 87. Jiménez A-F, Cárdenas P-F, Jiménez F. Smart water management approach for resource allocation in high-scale irrigation systems. Agric Water Manag. 2021;256:107088. https://doi.org/10.1016/j.agwat.2021.107088
  88. 88. Durodola OS, Mourad KA. Modelling maize yield and water requirements under different climate change scenarios. Clim. 2020 ;8(11):127. https://doi.org/10.3390/cli8110127
  89. 89. Ogra M, Manral U, Platt RV, Badola R, Butcher L. Local perceptions of change in climate and agroecosystems in the Indian Himalayas: A case study of the Kedarnath Wildlife Sanctuary (KWS) landscape, India. Appl Geogr. 2020;125:102339. https://doi.org/10.1016/j.apgeog.2020.102339
  90. 90. Bhatt R, Singh J, Laing AM, Meena RS, Alsanie WF, Gaber A, et al. Potassium and water-deficient conditions influence the growth, yield and quality of ratoon sugarcane (Saccharum officinarum L.) in a semi-arid agroecosystem. Agronom. 2021;11(11):2257. https://doi.org/10.3390/agronomy11112257
  91. 91. Keller T, Défossez P, Weisskopf P, Arvidsson J, Richard G. SoilFlex: A model for prediction of soil stresses and soil compaction due to agricultural field traffic including a synthesis of analytical approaches. Soil Tillage Res. 2007;93(2):391–411. https://doi.org/10.1016/j.still.2006.05.012
  92. 92. Hari M, Tyagi B, Huddar MS, Harish A. Satellite‐based regional‐scale evapotranspiration estimation mapping of the rice bowl of Tamil Nadu: A little water to spare. Irrig Drain. 2021;70(4):958–75. https://doi.org/10.1002/ird.2553
  93. 93. Mahule A, Sawarkar AD, Pakle G, Pachlor R, Singh L. AquaBamboo data-driven suggested system for water management and sustainable growth of bamboo: A review. Adv Bamboo Sci. 2024;7:100072. https://doi.org/10.1016/j.bamboo.2024.100072
  94. 94. Ahmadi A, Kazemi MH, Daccache A, Snyder RL. SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation. Agric Water Manag. 2024;295:108779. https://doi.org/10.1016/j.agwat.2024.108779
  95. 95. Manjunatha B, Kumar KD, Goundar S, Kavin BP, Seng GH. Sustainable waste management OOA-enhanced MobileNetV2-TC model for trash image classification. In: Kumar D, Vijayakumar V, Nidal N, Poluru RK, editors. Computational intelligence for green cloud computing and digital waste management. IGI Global. 2024;227–47. https://doi.org/10.4018/979-8-3693-1552-1.ch012
  96. 96. Zhu S, Cui N, Jin H, Jin X, Guo L, Jiang S, et al. Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors. Agric Water Manag. 2024;294:108705. https://doi.org/10.1016/j.agwat.2024.108705
  97. 97. Dolaptsis K, Pantazi XE, Paraskevas C, Arslan S, Tekin Y, Bantchina BB, et al. A hybrid LSTM approach for irrigation scheduling in maize crop. Agri. 2024 ;14(2):210. https://doi.org/10.3390/agriculture14020210
  98. 98. Yang Y, Guan H, Batelaan O, McVicar TR, Long D, Piao S, et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci Rep. 2016;6(1):23284. https://doi.org/10.1038/srep23284
  99. 99. Padhiary M, Saha D, Kumar R, Sethi LN, Kumar A. Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicles for farm automation. Smart Agric Technol. 2024 :100483. https://doi.org/10.1016/j.atech.2024.100483
  100. 100.Rajurkar C, Prabaharan SR, Muthulakshmi S. IoT based water management. In: 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2), Chennai, India; 2017. p. 2559. https://doi.org/10.1109/ICNETS2.2017.8067943
  101. 101.Asli KH, Asli KH. Smart Water System and Internet of Things. J Mod Ind Manuf. 2023;2(5).
  102. 102.Pagano A, Amato F, Ippolito M, De Caro D, Croce D, Motisi A, et al. Internet of Things and Artificial Intelligence for Sustainable Agriculture: A Use Case in Citrus Orchards. 2023 IEEE 9th World Forum on Internet of Things (WF-IoT), Aveiro, Portugal, 2023; p. 1–6, https://doi.org/10.1109/WF-IoT58464.2023.10539593
  103. 103.Longo-Minnolo G, D’Emilio A, Vanella D, Consoli S. Advancing in satellite-based models coupled with reanalysis agrometeorological data for improving the irrigation management under the European Water Framework Directive. Agric Water Manag. 2024;301:108955. https://doi.org/10.1016/j.agwat.2024.108955
  104. 104.Prasad S. Shortages in agriculture labour market and changes in cropping pattern. In: Bathla S, Dubey A, editors. Changing contours of Indian agriculture. Singapore: Springer; 2017. p. 181–204. https://doi.org/10.1007/978-981-10-6014-4_11
  105. 105.Quddus A, Kropp JD. Constraints to agricultural production and marketing in the lagging regions of Bangladesh. Sustainability. 2020;12(10):3956. https://doi.org/10.3390/su12103956
  106. 106.Ebi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, et al. Extreme weather and climate change: population health and health system implications. Annu Rev Public Health. 2021;42(1):293–315. https://doi.org/10.1146/annurev-publhealth-012420-105026
  107. 107.Foster L, Szilagyi K, Wairegi A, Oguamanam C, de Beer J. Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants and gender. Smart Agric Technol. 2023;3:100132. https://doi.org/10.1016/j.atech.2022.100132
  108. 108.Debangshi U, Sadhukhan A, Dutta D, Roy S. Application of smart farming technologies in sustainable agriculture development: A comprehensive review on present status and future advancements. Int J Environ Clim Change. 2023;13(11):3689–704. https://doi.org/10.9734/ijecc/2023/v13i113549
  109. 109.Amiri-Zarandi M, Dara RA, Duncan E, Fraser ED. Big data privacy in smart farming: A review. Sustainability. 2022;14(15):9120. https://doi.org/10.3390/su14159120
  110. 110.Assimakopoulos F, Vassilakis C, Margaris D, Kotis K, Spiliotopoulos D. AI and related technologies in the fields of smart agriculture: A review. Info. 2025;16(2):100. https://doi.org/10.3390/info16020100
  111. 111.Khan S, Sachan HK, Krishna D. The role of smart farming technologies in mitigating climate change and enhancing agricultural sustainability. Int J Environ Clim Change. 2025;15(2):138–59. http://dx.doi.org/10.9734/ijecc/2025/v15i24718

Downloads

Download data is not yet available.