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

Review Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Sustainable vegetable production through smart farming: Innovative technologies and practices: Review article

DOI
https://doi.org/10.14719/pst.12430
Submitted
4 November 2025
Published
01-07-2026

Abstract

Smart farming is rapidly redefining agricultural practices by integrating modern technologies into conventional systems. In vegetable crop cultivation, these innovations offer context-specific solutions to improve productivity, resource-use efficiency and climate resilience. However, despite their proven potential, the adoption of smart farming in developing countries remains limited due to persistent infrastructural constraints, high initial investment costs, inadequate technical capacity, fragmented institutional support and policy gaps that hinder technology accessibility and scalability. Addressing these interconnected barriers forms the central problem addressed in this review. In contrast to studies that mainly address individual technologies or specific production systems, this review presents a comprehensive synthesis of smart farming applications across diverse vegetable cultivation systems, including greenhouse production, hydroponics, aeroponics, vertical farming and disease forecasting models. It uniquely integrates technological, socio-economic, infrastructural and policy perspectives to provide a holistic understanding of adoption challenges and opportunities. The study is organised into several key sections: it begins with an introduction to the relevance of smart farming, followed by a description of the review methodology. The core sections discuss available smart technologies and their practical applications in vegetable cultivation. Subsequent sections explore the ethical, environmental and socio-economic implications of these technologies, as well as the challenges hindering widespread adoption. The review concludes by identifying key research gaps and proposing future directions to support inclusive, scalable and sustainable implementation of smart farming practices.

References

  1. 1. World Bank. World Bank open data. 2020. https://data.worldbank.org
  2. 2. Anilkumar P, Ganesh K. Adoption of smart farming technologies in India: Opportunities and challenges. Int J Agric Manag Dev. 2017;7(2):123–35.
  3. 3. Gupta M, Abdelsalam M, Khorsandroo S, Mittal S. Security and privacy in smart farming: Challenges and opportunities. IEEE Access. 2020;8:34564–84. https://doi.org/10.1109/ACCESS.2020.2975142
  4. 4. Praveen B, Sharma P. A review of literature on climate change and its impacts on agriculture productivity. J Public Aff. 2019;19(4):e1960. https://doi.org/10.1002/pa.1960
  5. 5. Matson PA, Parton WJ, Power AG, Swift MJ. Agricultural intensification and ecosystem properties. Science. 1997;277(5325):504–9. https://doi.org/10.1126/science.277.5325.504
  6. 6. DeFries R, Fanzo J, Remans R, Palm C, Wood S et al. Metrics for land-scarce agriculture. Science. 2015;349(6245):238–40. https://doi.org/10.1126/science.aaa5766
  7. 7. Ojha T, Misra S, Raghuwanshi NS. Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Comput Electron Agric. 2015;118:66–84. https://doi.org/10.1016/j.compag.2015.08.011
  8. 8. Balafoutis AT, Beck B, Fountas S, Tsiropoulos Z, Vangeyte J, van der Wal T et al. Smart farming technologies: Description, taxonomy and economic impact. In: Pedersen M, Lind KM, editors. Precision agriculture: Technology and economic perspectives. Springer International Publishing; 2017. p. 21–77. https://doi.org/10.1007/978-3-319-68715-5_2
  9. 9. Kamilaris A, Kartakoullis A, Prenafeta-Boldu FX. A review on the practice of big data analysis in agriculture. Comput Electron Agric. 2017;143:23–37. https://doi.org/10.1016/j.compag.2017.09.037
  10. 10. Pivoto D, Waquil PD, Talamini E, Finocchio CPS, Dalla Corte VF, de Vargas Mores G. Scientific development of smart farming technologies and their application in Brazil. Inf Process Agric. 2018;5(1):21–32. https://doi.org/10.1016/j.inpa.2017.12.002
  11. 11. Burg S, Rist S, Delgado C. Emerging trends in digital agriculture: Understanding risks and opportunities for smallholder farmers. Agric Syst. 2019;174:102–12. https://doi.org/10.1016/j.agsy.2019.102012
  12. 12. Jayne TS, Snapp S, Place F, Sitko N. Sustainable agricultural intensification in an era of rural transformation in Africa. Glob Food Sec. 2019;20:105–13. https://doi.org/10.1016/j.gfs.2019.01.008
  13. 13. Moysiadis V, Sarigiannidis P, Vitsas V, Khelifi A. Smart farming in Europe. Comput Sci Rev. 2021;39:100345. https://doi.org/10.1016/j.cosrev.2020.100345
  14. 14. Sharma A, Georgi M, Tregubenko M, Tselykh A, Tselykh A. Enabling smart agriculture by implementing artificial intelligence and embedded sensing. Comput Ind Eng. 2022;165:107936. https://doi.org/10.1016/j.cie.2022.107936
  15. 15. Zhao J, Liu D, Huang R. A review of climate-smart agriculture: Recent advancements, challenges and future directions. Sustainability. 2023;15(4):3404. https://doi.org/10.3390/su15043404
  16. 16. Triantafyllou A, Tsouros DC, Sarigiannidis P, Bibi S. An architecture model for smart farming. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE; 2019. p. 385–92. https://doi.org/10.1109/DCOSS.2019.00081
  17. 17. Soussi A, Zero E, Sacile R, Trinchero D, Fossa M. Smart sensors and smart data for precision agriculture: A review. Sensors. 2024;24(8):2647. https://doi.org/10.3390/s24082647
  18. 18. Faqir Y, Qayoom A, Erasmus E, Schutte-Smith M, Visser HG. A review on the application of advanced soil and plant sensors in the agriculture sector. Comput Electron Agric. 2024;226:109385. https://doi.org/10.1016/j.compag.2024.109385
  19. 19. Farooq M, Hussain M, Siddique KHM. Digital technologies for sustainable agriculture: New paradigms in agronomy research and extension. Agronomy. 2020;10(2):207.
  20. 20. Mishra R, Singh R. A review on smart farming techniques for sustainable agriculture. J Clean Prod. 2022;367:133032.
  21. 21. Srivastava S, Arora D, Tiwari S. Enhancing sugarcane crop protection with IoT-based smart farming monitoring system. In: International Conference on Computer Vision and Robotics. Singapore: Springer Nature Singapore; 2024. p. 469–78. https://doi.org/10.1007/978-981-97-8868-2_37
  22. 22. Garcia-Ramos FJ, Vidal M, Bone A, Malon H, Aguirre J. Analysis of the air flow generated by an air-assisted sprayer equipped with two axial fans using a 3D sonic anemometer. Sensors. 2012;12(6):7598–613. https://doi.org/10.3390/s120607598
  23. 23. Halim AAA, Hassan NM, Zakaria A, Kamarudi LM, Bakar A. Internet of things technology for greenhouse monitoring and management system based on wireless sensor network. ARPN J Eng Appl Sci. 2016;11(22):13169–75.
  24. 24. Zeng L, Li D. Development of in situ sensors for chlorophyll concentration measurement. J Sens. 2015;2015:903509. https://doi.org/10.1155/2015/903509
  25. 25. Kapse S, Kale S, Bhongade S, Sangamnerkar S, Gotmare Y. IoT enable soil testing and NPK nutrient detection. J Compos Theory. 2020;13(V):314.
  26. 26. Vera J, Conejero W, Mira-Garcia AB, Conesa MR, Ruiz-Sanchez MC. Towards irrigation automation based on dielectric soil sensors. J Hortic Sci Biotechnol. 2021;96(6):696–707. https://doi.org/10.1080/14620316.2021.1906761
  27. 27. Moureaux C, Ceschia E, Arriga N, Beziat P, Eugster W, Kutsch WL et al. Eddy covariance measurements over crops. In: Aubinet M, Vesala T, Papale D, editors. Eddy covariance: A practical guide to measurement and data analysis. Springer; 2012. p. 319–32. https://doi.org/10.1007/978-94-007-2351-1_12
  28. 28. Yew TK, Yusoff Y, Sieng LK, Lah HC, Majid H, Shelida N. An electrochemical sensor ASIC for agriculture applications. In: Proc 37th Int Conv Inf Commun Technol Electron Microelectron (MIPRO). Opatija, Croatia; 2014. p. 85–90. https://doi.org/10.1109/MIPRO.2014.6859538
  29. 29. Yunus MAM, Mukhopadhyay SC. Novel planar electromagnetic sensors for detection of nitrates and contamination in natural water sources. IEEE Sens J. 2011;11(6):1440–7. https://doi.org/10.1109/JSEN.2010.2091953
  30. 30. Jaichandran R, Rajaprakash S, Karthik K, Somasundaram K. Prototype for effective utilization of available well water resource to irrigate multiple agriculture field effectively. Int J Appl Eng Res. 2017;12(19):8487–91.
  31. 31. Weiss U, Biber P. Plant detection and mapping for agricultural robots using a 3D-LIDAR sensor. Robot Auton Syst. 2011;59(5):265–73. https://doi.org/10.1016/j.robot.2011.02.011
  32. 32. Sureephong P, Wiangnak P, Wicha S. The comparison of soil sensors for integrated creation of IoT-based wetting front detector (WFD) with an efficient irrigation system to support precision farming. In: Proc Int Conf Digit Arts Media Technol (ICDAMT). IEEE; 2017. p. 132–5. https://doi.org/10.1109/ICDAMT.2017.7904949
  33. 33. Singh TA, Chandra J. IoT based greenhouse monitoring system. J Comput Sci. 2018;14(5):639–44. https://doi.org/10.3844/jcssp.2018.639.644
  34. 34. Jagadesh T, Sangeetha K, Sarvinprabhu R, Jagadesh R, Varshinee D. IoT based smart fertilizer management system. In: J Phys Conf Ser. 2021;1916(1):012198:25–6. https://doi.org/10.1088/1742-6596/1916/1/012198
  35. 35. Migdall S, Klug P, Denis A, Bach H. The additional value of hyperspectral data for smart farming. In: Proc IEEE Int Geosci Remote Sens Symp (IGARSS). 2012:7329–32. https://doi.org/10.1109/IGARSS.2012.6351937
  36. 36. Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture. 2022;12(10):1745. https://doi.org/10.3390/agriculture12101745
  37. 37. Aryan R, Mishra A, Kumar S, Kumari M. A smart farming and crop monitoring technology in agriculture using IoT. Int J Res Appl Sci Eng Technol. 2022;10(7):1072–80. https://doi.org/10.22214/ijraset.2022.42409
  38. 38. Hemma A, Binandeh AR, Ghaisari J, Khorsand A. Development and field testing of an integrated sensor for on-the-go measurement of soil mechanical resistance. Sens Actuators A Phys. 2013;198:61–8. https://doi.org/10.1016/j.sna.2013.04.027
  39. 39. Vila J, Calpe J, Pla F, Gomez L, Connell J, Marchant J. SmartSpectra team. SmartSpectra: Applying multispectral imaging to industrial environments. Real-Time Imaging. 2005;11(2):85–98. https://doi.org/10.1016/j.rti.2005.04.007
  40. 40. Andujar D, Ribeiro A, Fernandez-Quintanilla C, Dorado J. Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops. Sensors. 2011;11(3):2304–18. https://doi.org/10.3390/s110302304
  41. 41. Ananthi N, Divya J, Divya M, Janani V. IoT based smart soil monitoring system for agricultural production. In: Proc IEEE Technol Innov ICT Agric Rural Dev (TIAR). Chennai: IEEE; 2017 Apr 7–8:209–14. https://doi.org/10.1109/TIAR.2017.8273717
  42. 42. Izquiendo J, Lopez R, Fernandez J. Use of drones in precision agriculture: Advances and perspectives. Comput Electron Agric. 2018;149:262–73.
  43. 43. Schuster JN, Darr MJ, McNaull RP. Performance benchmark of yield monitors for mechanical and environmental influences. In: Agric Biosyst Eng Conf Proc Presentations. Ames, IA: Iowa State University; 2017:1–8.
  44. 44. Yalew SG, van Griensven A, Mul ML, van der Zaag P. Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Model Earth Syst Environ. 2016;2:101. https://doi.org/10.1007/s40808-016-0167-x
  45. 45. Crabit A, Colin F, Bailly JS, Ayroles H, Garnier F. Soft water level sensors for characterizing the hydrological behaviour of agricultural catchments. Sensors. 2011;11(5):4656–67. https://doi.org/10.3390/s110504656
  46. 46. Lamprinos I, Charalambides M. Experimental assessment of ZigBee as the communication technology of a wireless sensor network for greenhouse monitoring. Int J Adv Smart Sens Netw Syst. 2015;6(1):1–10. https://doi.org/10.5121/ijassn.2015.5401
  47. 47. Ashwini BV. A study on smart irrigation system using IoT for surveillance of crop-field. Int J Eng Technol. 2018;4(5):370–3. https://doi.org/10.14419/ijet.v7i4.5.20109
  48. 48. Comegna A, Di Prima S, Hassan SBM, 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
  49. 49. Kodali RK, Vishal J, Karagwal S. IoT-based smart greenhouse. In: Proc IEEE Reg Humanitarian Technol Conf (R10-HTC). 2016:1–6.
  50. 50. Palande V, Zaheer A, George K. Fully automated hydroponic system for indoor plant growth. Procedia Comput Sci. 2018;129:482–8. https://doi.org/10.1016/j.procs.2018.03.028
  51. 51. Yan M, Liu P, Zhao R. Field microclimate monitoring system based on wireless sensor network. J Intell Fuzzy Syst. 2018;35:1–13. https://doi.org/10.3233/JIFS-169676
  52. 52. Das A De, Pramanik A. Evolution of E-sensing technology. In: Proc Int Conf Front Comput Syst (COMSYS 2020). Singapore: Springer; 2020:565–77. https://doi.org/10.1007/978-981-15-7834-2_53
  53. 53. Xu M, Wang J, Zhu L. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chem. 2019;289:482–9. https://doi.org/10.1016/j.foodchem.2019.03.080
  54. 54. Bhattacharyya N, Bandhopadhyay R. Electronic nose and electronic tongue. In: Sun D, ed. Non-destructive Evaluation of Food Quality: Theory and Practice. Berlin, Heidelberg: Springer; 2010:73–100. https://doi.org/10.1007/978-3-642-15796-7_4
  55. 55. Panteleev D, Khomich E, Evstratov V. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors. 2015;15(2):2604–21.
  56. 56. Gomez AH, Hu G, Wang J, Pereira AG. Evaluation of tomato maturity by electronic nose. Comput Electron Agric. 2006;54(1):44–52. https://doi.org/10.1016/j.compag.2006.07.002
  57. 57. Rutolo MF, Iliescu D, Clarkson JP, Covington JA. Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biol Technol. 2016;116:50–8. https://doi.org/10.1016/j.postharvbio.2015.12.028
  58. 58. Dorji U, Pobkrut T, Kerdcharoen T. Electronic nose based wireless sensor network for soil monitoring in precision farming system. In: Proc 9th Int Conf Knowledge Smart Technol (KST). IEEE; 2017 Feb 1–4:182–6. https://doi.org/10.1109/KST.2017.7886087
  59. 59. Fekete D, Balazs G, Bohm V, Varvolgyi E, Kappel N. Sensory evaluation and electronic tongue for sensing grafted and non-grafted watermelon taste attributes. Acta Aliment. 2018;47(4):487–94. https://doi.org/10.1556/066.2018.47.4.12
  60. 60. Mennecke BE, Crossland MD. Geographic information systems: Applications and research opportunities for information systems researchers. In: Proc HICSS-29: 29th Hawaii Int Conf Syst Sci. IEEE; 1996 Jan 3–6:537–46. https://doi.org/10.1109/HICSS.1996.493249
  61. 61. Abdel-Ghany HM, Alam SM, Ahmed M, Hassan SI. Fundamentals of precision agriculture for vegetable crops. NC State Extension Publications. 2024. https://content.ces.ncsu.edu/fundamentals-of-precision-agriculture-for-vegetable-crops
  62. 62. Ojo OI, Ilunga F. Geospatial analysis for irrigated land assessment, modeling and mapping. In: Rustamov RB, Hasanova S, Zeynalova MH, eds. Multi-purposeful application of geospatial data. IntechOpen; 2018:65–84. https://doi.org/10.5772/intechopen.73314
  63. 63. Fuentes RM, Fuentes ET, Quintana SE, Garcia-Zapateiro LA. Application of geographic information systems for characterization of preharvest and postharvest factors of squash (Cucurbita sp.) in Bolívar Department, Colombia. Indian J Sci Technol. 2018;11(9):1–10. https://doi.org/10.17485/ijst/2018/v11i9/117914
  64. 64. Zakarya YM, Metwaly MM, Abdel Rahman MA, Metwalli MR, Koubouris G. Optimized land use through integrated land suitability and GIS approach in West El-Minia Governorate, Upper Egypt. Sustainability. 2021;13(21):12236. https://doi.org/10.3390/su132112236
  65. 65. Tugrul O. Precision horticulture: GIS-guided vigor zoning and spatial spray management in cucumber cultivation. J Precis Hortic. 2023;4(1):22–34.
  66. 66. Yao W, Long T, Wu X. A systematic review of GIS applications in agricultural pest management: current status, challenges and future trends. Agric Syst. 2023;210:103704.
  67. 67. Swinton SM, Basso B. Economic profitability of variable rate nitrogen prescriptions for leafy vegetable systems. Calif Agric. 2025;79(2):112–19.
  68. 68. Lang L. GPS + GIS + remote sensing: an overview. Earth Obs Mag. 1992;4:23–26.
  69. 69. Batte MT, Van Buren F. Precision farming-factors influencing profitability. In: Northern Ohio Crops Day Meeting. Wood County, OH: Ohio State University Extension; 1999. p. 1–14.
  70. 70. Laboski CAM, Peters JB. Sampling soils for testing. University of Wisconsin-Madison Extension; 2012. p. 1–24.
  71. 71. Slaughter DC, Perez Ruiz M, Fathallah F, Upadhyaya S, Gliever CJ, Miller B. GPS-based intra-row weed control system: performance and labor savings. In: Rovira F, editor. Automation Technology for Off-Road Equipment. Spain; 2012. p. 1–12.
  72. 72. Reckleben Y, Grau T, Schulz S, Trumpf HG. Effects of precision potato planting using GPS-based cultivation. Adv Anim Biosci. 2017;8(2):450–54. https://doi.org/10.1017/S2040470017000036
  73. 73. Bauer A, Bostrom AG, Ball J, Applegate C, Cheng T, Laycock S. AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning. BioRxiv. 2019;527184. https://doi.org/10.1101/527184
  74. 74. Mudda S. GIS/GPS-based precision agriculture model for small vegetable farms. Agribus Inf Manag. 2022;9(1):22–30.
  75. 75. Mishra RK, Sarmah AK, Yadav J, Singh R. Precision nutrient management using variable rate technology in potato cultivation. J Soil Sci Plant Nutr. 2023;23(3):4443–60.
  76. 76. PolyEyes Staff. Precision farming in India using GPS and drones. PolyEyes AgriTech Reports. 2025.
  77. 77. Kumar P, Singh DP, Verma N, Sharma PK. Precision farming in horticultural crops. Int J Adv Biochem Res. 2025;9(2):90–95. https://doi.org/10.33545/26646781.2025.v7.i11b.336
  78. 78. Bhattarai U, Arikapudi R, Peng C, Fennimore SA, Martin FN, Vougioukas SG. Precision yield mapping in manual vegetable harvesting using GPS-instrumented carts: a lettuce case study. Precis Agric. 2025;26(2):301–15.
  79. 79. Hussain B, War AR, Pfeiffer DG. Mapping foliage damage index and monitoring of Pieris brassicae by using GIS-GPS technology in cole crops. J Entomol Zool Stud. 2018;6(2):933–38.
  80. 80. Mohsan SAH, Khan MA, Noor F, Ullah I, Alsharif MH. Towards the unmanned aerial vehicles (UAVs): a comprehensive review. Drones. 2022;6(6):147. https://doi.org/10.3390/drones6060147
  81. 81. Sarker IH, Khan NI, Rayhan F, Chowdhury MA, Rahman MA. Drones in plant disease assessment, efficient monitoring and detection: a way forward to smart agriculture. Agronomy. 2025;15(1):108.
  82. 82. Walter A, Finger R, Hube R, Buchmann N. Smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci U S A. 2017;114(24):6140–50. https://doi.org/10.1073/pnas.1707462114
  83. 83. Nourmohammadi A, Jafari M, Zander TO. A survey on unmanned aerial vehicle remote control using brain-computer interface. IEEE Trans Hum-Mach Syst. 2018;48(4):337–48. https://doi.org/10.1109/THMS.2018.2830647
  84. 84. Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 2019;24:152–64. https://doi.org/10.1016/j.tplants.2018.11.007
  85. 85. Oksana D, Volodymyr M, Oleg S. Accounting automation in agro-industrial enterprises using drones. Adv Comput Inf Techno. 2021:337–41. https://doi.org/10.1109/ACIT52158.2021.9548424
  86. 86. Pathak H, Kumar G, Mohapatra SD, Gaikwad BB, Rane J. Use of drones in agriculture: potentials, problems and policy needs. ICAR-National Institute of Abiotic Stress Management. 2020:4–15.
  87. 87. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming-a review. Agric Syst. 2017;153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023
  88. 88. Abbas A, Zhang Z, Zheng H, Alami MM, Alrefaei AF, Abbas Q, et al. Drones in plant disease assessment, efficient monitoring and detection: a way forward to smart agriculture. Agronomy. 2023;13(6):1524. https://doi.org/10.3390/agronomy13061524
  89. 89. Salami E, Barrado C, Pastor E. UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sens. 2014;6(11):11051–81. https://doi.org/10.3390/rs61111051
  90. 90. Niu H, Zhao T, Wan D, Chen Y. A UAV resolution and waveband aware path planning for onion irrigation treatments inference. In: Proc Int Conf Unmanned Aircraft Syst (ICUAS). Atlanta, GA; 2019. p. 808–12. https://doi.org/10.1109/ICUAS.2019.8798188
  91. 91. Martins S, Lhissou R, Chokmani K, Cambouris A. Determining the beginning of potato tuberization period using plant height detected by drone for irrigation purposes. Agronomy. 2023;13(2):492. https://doi.org/10.3390/agronomy13020492
  92. 92. Akbari A, Majidi M, Talebi Jahromi K, Hatami B. Using drone for chemical control of cabbage aphid, Brevicoryne brassicae L. (Hemiptera: Aphididae) in canola fields. J Entomol Soc Iran. 2023;25(2):1–18.
  93. 93. Broussard MA, Coates M, Martinsen P. Artificial pollination technologies: a review. Agronomy. 2023;13(5):1351. https://doi.org/10.3390/agronomy13051351
  94. 94. Bishop R. A survey of intelligent vehicle applications worldwide. In: Proc IEEE Intell Veh Symp. USA; 2000. p. 25–30. https://doi.org/10.1109/IVS.2000.898313
  95. 95. De Santos PG, Armada MA, Jimenez A. Agricultural robotics: unmanned systems for smart farming. Robot Auton Syst. 2020;125:103412.
  96. 96. Lowenberg-DeBoer J, Huang IY, Grigoriadis V, Blackmore S. Economics of robots and automation in field crop production. Precis Agric. 2021;21(3):278–99. https://doi.org/10.1007/s11119-019-09667-5
  97. 97. Rakshitha N, Rekha HS, Sandhya S, Sandhya V, Sowndeswari S. Pepper cutting UGV and disease detection using image processing. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information and Technology (RTEICT); 2017. p. 950–52. https://doi.org/10.1109/RTEICT.2017.8256738
  98. 98. Bogoescu M, Doltu M, Singh M, Iordache B. Digital robotic system for grafting vegetable seedlings. In: Proc. Int. Symp. Adv. Technol. Manage. Innov. Greenhouses: GreenSys. 2019;1296:1071–78. https://doi.org/10.17660/ActaHortic.2020.1296.135
  99. 99. Roldan-Gomez R, Arzamendia M, Salmoral A, Sanz R. Autonomous weeding robots: current trends and future challenges. Precis Agric. 2023;24(5):1629–58.
  100. 100. FarmBot. Fresh veggies grown in your own backyard. https://farm.bot
  101. 101. Yuan T, Zhang S, Sheng X, Wang D, Gong Y, Li W. An autonomous pollination robot for hormone treatment of tomato flower in greenhouse. In: Proc. Int. Conf. Syst Inform (ICSAI). Shanghai, China: IEEE; 2016:108–13. https://doi.org/10.1109/ICSAI.2016.7810939
  102. 102. TTA-ISO. TTA-ISO introduces fully automated tomato harvesting robot. Future Farming. 2024. https://www.futurefarming.com/farm-management/robots/tta-iso-introduces-fully-automated-tomato-harvesting-robot/
  103. 103. Fraunhofer Institute for Production Systems and Design Technology – Automation (Fraunhofer IPK). Automated fruit and vegetable harvesting. https://www.ipk.fraunhofer.de
  104. 104. Singh R, Verma D, Choudhary N. Smart framework for IoT-based soil moisture prediction and irrigation automation in greenhouse tomato. Front Plant Sci. 2023;14:1239594.
  105. 105. O'Grady M, O'Hare G. Modelling the smart farm. Inf Process Agric. 2017;4(3):179–87. https://doi.org/10.1016/j.inpa.2017.05.001
  106. 106. Delgado F, Kim J, Morales P. GPS-guided variable rate nutrient management in lettuce fields: effects on nitrogen use efficiency. Precis Agric. 2024;25(3):415–30.
  107. 107. Mohanty N, Kumar A, Singh AK. An IoT-based watering system for tomato crop on smart agriculture. Comput Electron Agric. 2025;220:108845.
  108. 108. Doshi J, Patel T, Bharti SK. Smart farming using IoT, a solution for optimally monitoring farming conditions. Procedia Comput Sci. 2019;160:746–51. https://doi.org/10.1016/j.procs.2019.11.016
  109. 109. Kalathas J, Bandekas DV, Kosmidis A, Kanakaris V. Seedbed based on IoT: a case study. J Eng Sci Technol Rev. 2016;9(2):1–6.
  110. 110. Goap A, Sharma D, Shukla AK, Krishna CR. An IoT-based smart irrigation management system using machine learning and open-source technologies. Comput Electron Agric. 2018;155:41–49. https://doi.org/10.1016/j.compag.2018.09.040
  111. 111. Boersma S, van Mourik S. On sensor configurations in a lettuce greenhouse via observability analysis using the empirical gramian. Acta Hortic. 2025;1425:297–304. https://doi.org/10.17660/ActaHortic.2025.1425.38
  112. 112. Farmonaut Research Group. Sensor-driven IoT irrigation management in open-field tomato production in Bengaluru. Farmonaut Research Reports. 2025.
  113. 113. He G, Liu L, Wu J, Du X, Lv Y. A quality traceability system for fruit and vegetable supply chain based on multichain blockchain. Front Blockchain. 2024;7:1378174.
  114. 114. Leon-Garcia F, Calero-Gutierrez V, Calero-Martinez C, Garcia-Ferrer F, Calero R. Decision support systems and data analysis in insect pest e-monitoring and control. Appl Sci. 2024;14(22):10307.
  115. 115. de Luna RG, Dadios EP, Bandala AA, Vicerr RRP. Tomato growth stage monitoring for smart farm using deep transfer learning with machine learning-based maturity grading. AGRIVITA J Agric Sci. 2020;42(1):24–36. https://doi.org/10.17503/agrivita.v42i1.2499
  116. 116. Bassine L, Gupta R, Miah T. Drone and IoT sensor fusion for biomass and water stress prediction in lettuce cultivation. Agronomy. 2023;13(5):875.
  117. 117. Prusty AK, Saha P, Das N, Suman S. Implementation and adoption of smart technologies in agri-allied sectors. Plant Sci Today. 2025;11:3467. https://doi.org/10.14719/pst.3467
  118. 118. Sharma A, Gupta P, Singh R. IoT-based smart hydroponic system for nutrient management and plant growth monitoring: a review. Comput Electron Agric. 2024;216:108427.
  119. 119. Al-Quradaghi K, Al-Dulaimi R, Al-An AH, Mohammed HI. Machine learning-based prediction of tomato yield in greenhouse environments. Sensors. 2024;24(17):5486.
  120. 120. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU. The rise of “big data” on cloud computing: review and open research issues. Inf Syst. 2015;47:98–115. https://doi.org/10.1016/j.is.2014.07.006
  121. 121. AgWise Research Group. Sensor-driven IoT irrigation management in open-field tomato production in Bengaluru. AgWise Case Study Rep. 2022;22(3):12–20.
  122. 122. Tang L, Lu S. Big data-driven cold chain logistics optimization for spinach supply chains. PLoS One. 2025;20(3):e0319268.
  123. 123. Tang L, Lu S, Zhang Y. Layout optimization of multi-level cold chain storage facilities in agricultural producing areas considering type and capacity constraints. PLoS One. 2025;19(2):e0295843.
  124. 124. Watanabe K, Hirose M, Tanaka Y. Fuzzy PID and clustering-based cloud greenhouse automation for tomato yield improvement. J Smart Agric. 2023;9(3):211–25.
  125. 125. Dharani SB, Kumaraperumal R, Muthumanickam D, Chitra N, Kavitha S. Harnessing remote sensing for smart agriculture. Plant Sci Today. 2025;12(3):1–13. https://doi.org/10.14719/pst.9174
  126. 126. Garcia AM, Garcia IF, Poyato EC, Barrios PM, Diaz JR. Coupling irrigation scheduling with solar energy production in a smart irrigation management system. J Clean Prod. 2018;175:670–82. https://doi.org/10.1016/j.jclepro.2017.12.093
  127. 127. Liaqat UW, Choi M. Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images. Agric For Meteorol. 2015;215:60–79. https://doi.org/10.1016/j.agrformet.2015.08.245
  128. 128. Ahmad KA, Ahmad SA, Ahmad MJ, Shah A, Asghar MM. A review of evapotranspiration estimation methods for climate-smart agriculture tools under a changing climate: vulnerabilities, consequences and implications. J Water Clim Change. 2024;16(2):249–69. https://doi.org/10.2166/wcc.2024.048
  129. 129. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM. A remote sensing surface energy balance algorithm for land (SEBAL): formulation. J Hydrol. 1998;212–213:198–212. https://doi.org/10.1016/S0022-1694(98)00253-4
  130. 130. Allen RG, Tasumi M, Trezza R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) model. J Irrig Drain Eng. 2007;133:380–94. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
  131. 131. Su Z. The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sci. 2002;6:85–100. https://doi.org/10.5194/hess-6-85-2002
  132. 132. Cetin M, Alsenjar O, Aksu H, Golpinar MS, Akgul MA. Estimation of crop water stress index and leaf area index based on remote sensing data. Water Supply. 2023;23(3):1390–1404. https://doi.org/10.2166/ws.2023.051
  133. 133. Zhang C, Li W, Travis DJ. Spatiotemporal analysis of precision agriculture with UAVs. Remote Sens Environ. 2018;210:456–67.
  134. 134. Garg S, Pundir P, Jindal H, Saini H, Garg S. Towards a multimodal system for precision agriculture using IoT and machine learning. In: Proc 12th Int Conf Comput Commun Netw Technol (ICCCNT), IIT Kharagpur. IEEE. 2021:1–7. https://doi.org/10.1109/ICCCNT51525.2021.9579646
  135. 135. Ayoubian M, Ayoubian A. Climate control optimization in cucumber greenhouses using smart sensors. Int J Agric Technol. 2025;18(1):34–49.
  136. 136. Zamora-Izquierdo MA, Santa J, Martinez JA, Martinez V, Skarmeta AF. Smart farming IoT platform based on edge and cloud computing. Biosyst Eng. 2019;177:4–17. https://doi.org/10.1016/j.biosystemseng.2018.10.014
  137. 137. Pooja S, Uday DV, Nagesh UB, Talekar SG. Application of MQTT protocol for real-time weather monitoring and precision farming. In: Proc Int Conf Electr Electron Commun Comput Optim Technol (ICEECCOT). IEEE. 2017:1–6. https://doi.org/10.1109/ICEECCOT.2017.8284616
  138. 138. Shinde D, Siddiqui N. IoT based environment change monitoring and controlling in greenhouse using WSN. In: Proc Int Conf Inf Commun Eng Technol (ICICET), Zeal Coll Eng Res, Pune. 2018:1–5. https://doi.org/10.1109/ICICET.2018.8533808
  139. 139. Phupattanasilp P, Tong SR. Augmented reality IoT system with GIS overlays for greenhouse lettuce and cucumber monitoring. Comput Electron Agric. 2019;165:104935.
  140. 140. Rodriguez M, Patel A. Arduino-based IoT sensor networks for greenhouse microclimate control in tomato cultivation. Food Energy Secur. 2025;12(1):45–56.
  141. 141. Radhakrishnan G, Stephen R, Mithra VS. Optimizing vegetable fertigation with IoT and ML in controlled ecosystems. J Trop Agric. 2024;62(2):272–84.
  142. 142. Irfan S, Zhao L. Automated fertigation and nutrient delivery systems in greenhouse tomato using IoT and ML approaches: A review. J Electr Syst. 2024;20(4):289–300.
  143. 143. Sujati R, Pranoto A, Lestari D. Cloud-integrated hydroponic systems for leafy vegetable production. Indones J Smart Agric. 2023;7(1):89–102.
  144. 144. Wang Y, Li T, Chen T, Zhang H, Ren Y, Huang H, Huang X. Cucumber downy mildew disease prediction using a CNN-LSTM approach. Agriculture. 2024;14(7):1155. https://doi.org/10.3390/agriculture14071155
  145. 145. Poorna TK, Senthilkumar M, Manimekalai R, Saravanan PA, Vanitha G. Exploring the factors influencing the adoption of smart farming technologies in agriculture - A bibliometric analysis literature review. Plant Sci Today. 2025;12(3):1–14. https://doi.org/10.14719/pst.8325
  146. 146. Ferrandez-Pastor FJ, Garcia-Chamizo JM, Nieto-Hidalgo M, Mora-Pascual J, Mora-Martinez J. Developing ubiquitous sensor network platform using Internet of Things: application in precision agriculture. Sensors. 2016;16(7):1141. https://doi.org/10.3390/s16071141
  147. 147. Mehra M, Saxena S, Sankaranarayanan S, Tom RJ, Veeramanikandan M. IoT based hydroponics system using deep neural networks. Comput Electron Agric. 2018;155:473–86. https://doi.org/10.1016/j.compag.2018.10.015
  148. 148. Chowdhury ME, Khandakar A, Ahmed S, Khuzaei FA, Hamdalla J, Haque F, et al. Design, construction and testing of IoT based automated indoor vertical hydroponics farming test-bed in Qatar. Sensors. 2020;20(19):5637. https://doi.org/10.3390/s20195637
  149. 149. Lucero L, Lucero D, Mejia EO, Collaguazo G. Automated aeroponics vegetable growing system. Case study lettuce. IEEE Andescon. 2020:1–7. https://doi.org/10.1109/ANDESCON50619.2020.9272180
  150. 150. Omar S, Farid K, Chan L. Cloud-controlled IoT climate sensors in vertical lettuce farming: Growth and energy efficiency benefits. Comput Electron Agric. 2023;212:108001.
  151. 151. Babar MI, Akan OB. Internet of Everything (IoE) and nano-IoT applications for precision agriculture in leafy greens. IEEE Internet Things J. 2024;11(2):3345–62.
  152. 152. Sa I, Lehnert C, English A, McCool C, Dayoub F, Upcroft B, et al. Peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3-D information. IEEE Robot Autom Lett. 2017;2(2):765–72. https://doi.org/10.1109/LRA.2017.2651952
  153. 153. Lee U, Islam MP, Kochi N, Tokuda K, Nakano Y, Naito H, et al. An automated clip-type IoT camera-based tomato flower and fruit monitoring and harvest prediction system. Sensors. 2022;22(7):2456. https://doi.org/10.3390/s22072456
  154. 154. Abbas AA. Integration of remote sensing and GIS for precision agriculture: A case study. Int J Struct Des Eng. 2024;5(2):15–24.
  155. 155. Patel A, Shukla C, Trivedi A, Balasaheb KS, Sinha MK. Smart farming: Utilization of robotics, drones, remote sensing, GIS, AI and IoT tools in agricultural operations and water management. In: Integrated land and water resource management for sustainable agriculture. Springer Nature Singapore. 2025:127–51. https://doi.org/10.1007/978-981-97-9796-7_8
  156. 156. Caroline A, Yasmine AA, Deva M, Prathyusha KS, Shruti SR, Sowndarya CD. Real time quality detection of vegetables using sensors. Int J Eng Res Technol. 2020;7(6):3412–16.
  157. 157. Tervonen J. Experiment of the quality control of vegetable storage based on the internet-of-things. Proc Comput Sci. 2018;130:440–47. https://doi.org/10.1016/j.procs.2018.04.065
  158. 158. Potamitis I, Eliopoulos P, Rigakis I. Automated remote insect surveillance at a global scale and the Internet of Things. Robotics. 2017;6(19):1–14. https://doi.org/10.3390/robotics6030019
  159. 159. Rustia DJA, Lin TT. An IoT-based wireless imaging and sensor node system for remote greenhouse pest monitoring. Chem Eng. 2017;58:601–6.
  160. 160. Rupanagudi SR, Ranjani BS, Nagaraj P, Bhat VG, Thippeswamy G. A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In: ICCICT. 2015:1–6. https://doi.org/10.1109/ICCICT.2015.7045722
  161. 161. Bittner JA, Balfe S, Pittendrigh BR, Popovics JS. Monitoring of the Cowpea Bruchid, Callosobruchus maculatus (Coleoptera: Bruchidae), feeding activity in cowpea seeds: advances in sensing technologies reveals new insights. J Econ Entomol. 2018;111(3):1469–75. https://doi.org/10.1093/jee/toy086
  162. 162. Peng S, Yang S, Zhang X, Jia J, Chen Q, Lian Y, et al. Analysis of imidacloprid residues in mango, cowpea and water samples based on portable molecular imprinting sensors. PLoS One. 2021;16(9):e0257042. https://doi.org/10.1371/journal.pone.0257042
  163. 163. Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune EHM. Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access. 2019;7:129551–83. https://doi.org/10.1109/ACCESS.2019.2932609
  164. 164. Foughali K, Fathallah K, Frihida A. Using cloud IoT for disease prevention in precision agriculture. Proc Comput Sci. 2018;130:575–82. https://doi.org/10.1016/j.procs.2018.04.106
  165. 165. Jumat MH, Nazmudeen MS, Wan AT. Smart farm prototype for plant disease detection, diagnosis and treatment using IoT device in a greenhouse. BICET. 2018:48. https://doi.org/10.1049/cp.2018.1545
  166. 166. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU. The rise of 'big data' on cloud computing: Review and open research issues. Inf Syst. 2015;47:98–115. https://doi.org/10.1016/j.is.2014.07.006

Downloads

Download data is not yet available.