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

Review Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Robotics redefining wheat farming: Bridging efficiency and sustainability

DOI
https://doi.org/10.14719/pst.8322
Submitted
17 March 2025
Published
06-07-2025

Abstract

Automation in wheat cultivation has revolutionized precision, sustainability and overall productivity by integrating advanced robotics and cutting-edge technology. High-clearance robots automate all growth stages by employing adaptive Kalman filters and fuzzy PID controllers for precisely controlled navigation and acquisition of phenotypic data. Swarm robots are cost-effective and exhibit adaptability to varied field conditions challenging traditional economies of scale, enabling smaller farms to achieve competitive production costs. Advances in image processing have overcome the challenges of canopy closure, enabling sub-50 mm accuracy in wheat row tracking, critical for early-growth interventions. Integration of LiDAR, spectral sensors and aerial robotics complements ground-based systems, offering robust data for decision support. Deployment of mobile robots has enhanced precision seeding with accuracy reaching over 93 % while high-throughput phenotyping platforms utilize robotics and machine learning to transform disease resistance assessments, such as Fusarium Head Blight (FHB). Algorithms like DeepLabV3+ have achieved over 96 % accuracy in identifying wheat ears, significantly reducing labour in breeding resistant varieties. The seed screener platform automates the analysis of single wheat kernels, combining RGB imaging and near-infrared (NIR) spectroscopy to evaluate 3D morphological and biochemical traits. The seed screener uses the marching cubes algorithm to extract precise morphological data from 3D visual models. This high-precision, high-throughput platform demonstrates significant potential for commercialisation, providing breeders with an advanced tool to facilitate wheat improvement programmes. These innovations address critical challenges, including phenotypic characterisation, planting uniformity and real-time adaptability, offering transformative solutions for precision agriculture. Automation in wheat cultivation provides a pathway to achieving food security while ensuring sustainability, ushering in a new era in agricultural practices.

References

  1. 1. Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SC, Tester M, et al. Breeding crops to feed 10 billion. Nature Biotechnology. 2019;37(7):744–54. https://doi.org/10.1038/s41587-019-0152-9
  2. 2. Yannam VR, Soriano JM, Chozas A, Guzmán C, Lopes MS, Giraldo P. Genetic variability for end-use quality proteins in a collection of bread wheat Mediterranean landraces. Journal of Cereal Science. 2024;119:104002. https://doi.org/10.1016/j.jcs.2024.104002
  3. 3. Pérez-Pérez M, Ribeiro M, Fdez-Riverola F, Igrejas G. Insights into wheat science: a bibliometric review using unsupervised machine learning techniques. Journal of Cereal Science. 2024;119:103960. https://doi.org/10.1016/j.jcs.2024.103960
  4. 4. Liu T, Zhao Y, Sun Y, Wang J, Yao Z, Chen C, et al. High-throughput identification of Fusarium head blight resistance in wheat varieties using field robot-assisted imaging and deep learning techniques. Journal of Cleaner Production. 2024;480:144024. https://doi.org/10.1016/j.jclepro.2024.144024
  5. 5. Oliveira LF, Moreira AP, Silva MF. Advances in agriculture robotics: A state-of-the-art review and challenges ahead. Robotics. 2021;10(2):52. https://doi.org/10.3390/robotics10020052
  6. 6. De Clercq M, Vats A, Biel A. Agriculture 4.0: The future of farming technology. Proceedings of the World Government Summit, Dubai, UAE. 2018:11–3. https://doi.org/10.52783/eel.v14i1.1049
  7. 7. Alisaac E, Mahlein AK. Fusarium head blight on wheat: biology, modern detection and diagnosis and integrated disease management. Toxins. 2023;15(3):192. https://doi.org/10.3390/toxins15030192
  8. 8. Lysenko V, Opryshko O, Komarchuk D, Pasichnyk N, Zaets N, Dudnyk A. Usage of flying robots for monitoring nitrogen in wheat crops. In: 9th IEEE International conference on intelligent data acquisition and advanced computing systems: Technology and applications (IDAACS). 2017;1:30–4. https://doi.org/10.1109/IDAACS.2017.8095044
  9. 9. Neupane K, Baysal-Gurel F. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sensing. 2021;13(19):3841. https://doi.org/10.3390/rs13193841
  10. 10. de Castro AI, Shi Y, Maja JM, Peña JM. UAVs for vegetation monitoring: Overview and recent scientific contributions. Remote Sensing. 202;13(11):2139.https://doi.org/10.3390/rs13112139
  11. 11. Gunturu S, Munir A, Ullah H, Welch S, Flippo D. A spatial AI-based agricultural robotic platform for wheat detection and collision avoidance. AI. 2022;3(3):719–38. https://doi.org/10.3390/ai3030042
  12. 12. Li D, Nanseki T, Chomei Y, Kuang J. A review of smart agriculture and production practices in Japanese large‐scale rice farming. Journal of the Science of Food and Agriculture. 2023;103(4):1609–20. https://doi.org/10.1002/jsfa.12204
  13. 13. Yoshida T, Onishi Y, Kawahara T, Fukao T. Automated harvesting by a dual-arm fruit harvesting robot. ROBOMECH Journal. 2022;9(1):19. https://doi.org/10.1186/s40648-022-00233-9
  14. 14. Karkee M, Zhang Q. Fundamentals of Agricultural and Field Robotics. USA: Springer; 2021. https://doi.org/10.1007/978-3-030-70400-1
  15. 15. Lytridis C, Kaburlasos VG, Pachidis T, Manios M, Vrochidou E, Kalampokas T, et al. An overview of cooperative robotics in agriculture. Agronomy. 2021;11(9):1818. https://doi.org/10.3390/agronomy11091818
  16. 16. Zhang Q, Men X, Hui C, Ge F, Ouyang F. Wheat yield losses from pests and pathogens in China. Agriculture, Ecosystems & Environment. 2022;3(26):107821. https://doi.org/10.1016/j.agee.2021.107821
  17. 17. Yao L, Yuan H, Zhu Y, Jiang X, Cao W, Ni J. Design and testing of a wheeled crop-growth-monitoring robot chassis. Agronomy. 2023;13(12):3043. https://doi.org/10.3390/agronomy13123043
  18. 18. Kostavelis I, Charalampous K, Gasteratos A, Tsotsos JK. Robot navigation via spatial and temporal coherent semantic maps. Engineering Applications of Artificial Intelligence. 2016;48:173–87. https://doi.org/10.1016/j.engappai.2015.11.004
  19. 19. Balaska V, Bampis L, Kansizoglou I, Gasteratos A. Enhancing satellite semantic maps with ground-level imagery. Robotics and Autonomous Systems. 2021;139:103760.https://doi.org/10.1016/j.robot.2021.103760
  20. 20. Haibo L, Shuliang D, Zunmin L, Chuijie Y. Study and experiment on a wheat precision seeding robot. Journal of Robotics. 2015;1:696301. https://doi.org/10.1155/2015/696301
  21. 21. Naik NS, Shete VV, Danve SR. Precision agriculture robot for seeding function. In: International conference on inventive computation technologies (ICICT). IEEE. 2016; 2:1–3. https://doi.org/10.1109/INVENTIVE.2016.7824880
  22. 22. Zhang N, Pan Y, Feng H, Zhao X, Yang X, Ding C, et al. Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets. Biosystems Engineering. 2019;186:83–99. https://doi.org/10.1016/j.biosystemseng.2019.06.008
  23. 23. Almoujahed MB, Rangarajan AK, Whetton RL, Vincke D, Eylenbosch D, Vermeulen P, et al. Detection of Fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning. Computers and Electronics in Agriculture. 2022;203:107456. https://doi.org/10.1016/j.compag.2022.107456
  24. 24. Finger R, Swinton SM, El Benni N, Walter A. Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics. 2019;11(1):313–35. https://doi.org/10.1146/annurev-resource-100518-093929
  25. 25. Lowenberg-DeBoer J. The economics of precision agriculture. In: Precision Agriculture for Sustainability. Burleigh Dodds Science Publishing; 2019. p. 481–502. https://doi.org/10.1201/9781351114592
  26. 26. Reddy NV, Reddy AV, Pranavadithya S, Kumar JJ. A critical review on agricultural robots. International Journal of Mechanical Engineering and Technology. 2016;7(4):183–8.
  27. 27. Shah SK. A Review: Autonomous agribot for smart farming. Proceedings of 46th IRF International conference. 2015;50–3.
  28. 28. Del Cerro J, Cruz Ulloa C, Barrientos A, de León Rivas J. Unmanned aerial vehicles in agriculture: A survey. Agronomy. 2021;11(2):203.
  29. https://doi.org/10.3390/agronomy11020203
  30. 29. Rovira-Más F, Saiz-Rubio V, Cuenca-Cuenca A. Augmented perception for agricultural robots navigation. IEEE Sensors Journal. 2020;21(10):11712–27.
  31. https://doi.org/10.1109/JSEN.2020.3016081
  32. 30. Alsalam BH, Morton K, Campbell D, Gonzalez F. Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. In: IEEE Aerospace Conference, IEEE. 2017 ;1–12. https://doi.org/10.1109/aero.2017.7943593
  33. 31. Vahdanjoo M, Gislum R, Sørensen CA. Operational, economic, and environmental assessment of an agricultural robot in seeding and weeding operations. AgriEngineering. 2023;5(1):299–324. https://doi.org/10.3390/agriengineering5010020
  34. 32. Backman J, Linkolehto R, Lemsalu M, Kaivosoja J. Building a robot tractor using commercial components and widely used standards. IFAC-PapersOnLine. 2022;55(32):6–11. https://doi.org/10.1016/j.ifacol.2022.11.106
  35. 33. Yamasaki Y, Morie M, Noguchi N. Development of a high-accuracy autonomous sensing system for a field scouting robot. Computers and Electronics in Agriculture. 2022;193:106630. https://doi.org/10.1016/j.compag.2021.106630
  36. 34. Heidrich J, Gaulke M, Golling M, Alaydin BO, Barh A, Keller U. 324-fs Pulses from a SESAM Modelocked Backside-Cooled 2-μm VECSEL. IEEE Photonics Technology Letters. 2022;34(6):337–40. https://doi.org/10.1109/LPT.2022.3156181
  37. 35. Azmi HN, Hajjaj SS, Gsangaya KR, Sultan MT, Mail MF, Hua LS. Design and fabrication of an agricultural robot for crop seeding. Materials Today: Proceedings. 2023;81:283–9. https://doi.org/10.1016/j.matpr.2021.03.191
  38. 36. Krishnan A, Swarna S. Robotics, IoT, and AI in the automation of agricultural industry: a review. In: IEEE Bangalore Humanitarian Technology Conference (B-HTC) IEEE. 2020;1–6. https://doi.org/10.1109/B-HTC50970.2020.9297856
  39. 37. Martinez F, Romaine JB, Manzano JM, Ierardi C, Millan P. Deployment and verification of custom autonomous low-budget iot devices for image feature extraction in wheat. IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3453993
  40. 38. Berner B, Chojnacki J, Dvořák J, Pachuta A, Najser J, Kukiełka L, et al. Spraying wheat plants with a drone moved at low altitudes. Agronomy. 2024;14(9):1894. https://doi.org/10.3390/agronomy14091894
  41. 39. Cheng C, Fu J, Su H, Ren L. Recent advancements in agriculture robots: Benefits and challenges. Machines. 2023;11(1):48. https://doi.org/10.3390/machines11010048
  42. 40. Shandong YA, Chuang MA, Zhang B, Zhang Y, Jinchang YA. Design and experiment of no-tillage planter for high and low borders wheat. INMATEH-Agricultural Engineering. 2023;71(3). https://doi.org/10.35633/inmateh-71-36
  43. 41. Luo W, Chen X, Qin M, Guo K, Ling J, Gu F, et al. Design and experiment of uniform seed device for wide-width seeder of wheat after rice stubble. Agriculture. 2023;13(11):2173. https://doi.org/10.3390/agriculture13112173
  44. 42. Wei L, Wang Q, Niu K, Bai S, Wei L, Qiu C, et al. Design and test of seed–fertilizer replenishment device for wheat seeder. Agriculture. 2024;14(3):374. https://doi.org/10.3390/agriculture14030374
  45. 43. Gao S, Yuan Y, Zhang W, Zhao B, Zhou L, Deng X, et al. Trajectory planning and experimental research of seed box replenishment device robot arm for wheat seeder. SSRN 5081089. https://doi.org/10.2139/ssrn.5081089
  46. 44. Omidmehr Z. Evaluation of planter type and seed density on wheat yield in Kalpoosh dryland conditions. Iranian Dryland Agronomy Journal. 2024;13(1):48–63. https://doi.org/10.22092/idaj.2024.361668.396
  47. 45. Wang W, Shi W, Liu C, Wang Y, Liu L, Chen L. Development of automatic wheat seeding quantity control system based on Doppler radar speed measurement. Artificial Intelligence in Agriculture. 2025;15(1):12–25. https://doi.org/10.1016/j.aiia.2024.12.001
  48. 46. Mudarisov S, Badretdinov I, Rakhimov Z, Lukmanov R, Nurullin E. Numerical simulation of two-phase “Air-Seed” flow in the distribution system of the grain seeder. Computers and Electronics in Agriculture. 2020;168:105151. https://doi.org/10.1016/j.compag.2019.105151
  49. 47. Korohou T, Okinda C, Li H, Torotwa I, Ding Q, Abbas A. Effect of no-till precise seeding on wheat (Triticum aestivum L.) population quality at the emergence stage. JAPS: Journal of Animal & Plant Sciences. 2022;32(1). https://doi.org/10.36899/JAPS.2022.1.0414
  50. 48. Badgujar CM, Wu H, Flippo D, Brokesh E. Design, fabrication, and experimental investigation of screw auger type feed mechanism for a robotic wheat drill. Journal of the ASABE. 2022;65(6):1333–42. https://doi.org/10.13031/ja.15199
  51. 49. Xie B, Jin Y, Faheem M, Gao W, Liu J, Jiang H, et al. Research progress of autonomous navigation technology for multi-agricultural scenes. Computers and Electronics in Agriculture. 2023;211:107963. https://doi.org/10.1016/j.compag.2023.107963
  52. 50. Kanagasingham S, Ekpanyapong M, Chaihan R. Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot. Precision Agriculture. 2020;21(4):831–55. https://doi.org/10.1007/s11119-019-09697-z
  53. 51. Shi J, Bai Y, Diao Z, Zhou J, Yao X, Zhang B. Row detection BASED navigation and guidance for agricultural robots and autonomous vehicles in row-crop fields: Methods and applications. Agronomy. 2023;13(7):1780. https://doi.org/10.3390/agronomy13071780
  54. 52. Qu J, Zhang Z, Qin Z, Guo K, Li D. Applications of autonomous navigation technologies for unmanned agricultural tractors: A review. Machines. 2024;12(4):218. https://doi.org/10.3390/machines12040218
  55. 53. Ruan Z, Chang P, Cui S, Luo J, Gao R, Su Z. A precise crop row detection algorithm in complex farmland for unmanned agricultural machines. Biosystems Engineering. 2023;232:1–2. https://doi.org/10.1016/j.biosystemseng.2023.06.010
  56. 54. Liang Y, Zhou K, Wu C. Environment scenario identification based on GNSS recordings for agricultural tractors. Computers and Electronics in Agriculture. 2022;195:106829. https://doi.org/10.1016/j.compag.2022.106829
  57. 55. Jing Y, Li Q, Ye W, Liu G. Development of a GNSS/INS-based automatic navigation land levelling system. Computers and Electronics in Agriculture. 2023;213:108187. https://doi.org/10.1016/j.compag.2023.108187
  58. 56. Mahboub V, Mohammadi D. A constrained total extended Kalman filter for integrated navigation. The journal of navigation. 2018;71(4):971–88. https://doi.org/10.1017/S0373463318000012
  59. 57. 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
  60. 58. Mesías-Ruiz GA, Pérez-Ortiz M, Dorado J, De Castro AI, Peña JM. Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science. 2023;14:1143326. https://doi.org/10.3389/fpls.2023.1143326
  61. 59. Li H, Quan L, Guo Y, Pi P, Shi Y, Lou Z, et al. Improving agricultural robot patch-spraying accuracy and precision through combined error adjustment. Computers and Electronics in Agriculture. 2023;207:107755. https://doi.org/10.1016/j.compag.2023.107755
  62. 60. Feng ZH, Wang LY, Yang ZQ, Zhang YY, Li X, Song L, et al. Hyperspectral monitoring of powdery mildew disease severity in wheat based on machine learning. Frontiers in Plant Science. 2022;13:828454. https://doi.org/10.3389/fpls.2022.828454
  63. 61. Shruthi U, Nagaveni V, Raghavendra B. A review on machine learning classification techniques for plant disease detection. In: Proceedings of the 5th International Conference on Advanced Computing & Communication Systems (ICACCS); 2019; Coimbatore, India. p. 281–4. https://doi.org/10.1109/ICACCS.2019.8728415
  64. 62. Wei K, Chen B, Zhang J, Fan S, Wu K, Liu G, et al. Explainable deep learning study for leaf disease classification. Agronomy. 2022;12(5):1035. https://doi.org/10.3390/agronomy12051035
  65. 63. Mail MF, Maja JM, Marshall M, Cutulle M, Miller G, Barnes E. Agricultural harvesting robot concept design and system components: A review. Agri Engineering. 2023;5(2):777–800.https://doi.org/10.3390/agriengineering5020048
  66. 64. Atefi A, Ge Y, Pitla S, Schnable J. Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives. Frontiers in Plant Science. 2021;12:611940. https://doi.org/10.3389/fpls.2021.611940
  67. 65. Zhang Z, Ni X, Wu H, Sun M, Bao G, Wu H, et al. Pneumatically actuated soft gripper with bistable structures. Soft Robotics. 2022;9(1):57–71. https://doi.org/10.1089/soro.2019.0195
  68. 66. Astanakulov K, Shovazov K, Borotov A, Turdibekov A, Ibrokhimov S. Wheat harvesting by combine with GPS receiver and grain sensor. In: E3S Web of Conferences. 2021;227:07001. https://doi.org/10.1051/e3sconf/202122707001
  69. 67. Zhang K, Lammers K, Chu P, Li Z, Lu R. System design and control of an apple harvesting robot. Mechatronics. 2021;79:102644. https://doi.org/10.1016/j.mechatronics.2021.102644
  70. 68. Grimstad L, Pham CD, Phan HT, From PJ. On the design of a low-cost, light-weight, and highly versatile agricultural robot. In: 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts. 2015;1–6. https://doi.org/10.1109/ARSO.2015.7428210
  71. 69. Xiong Y, Ge Y, Grimstad L, From PJ. An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation. Journal of Field Robotics. 2020;37(2):202–24. https://doi.org/10.1002/rob.21889
  72. 70. Roshanianfard A, Noguchi N. Pumpkin harvesting robotic end-effector. Computers and Electronics in Agriculture. 2021;174:105503. https://doi.org/10.1016/j.compag.2020.105503
  73. 71. Schor N, Bechar A, Ignat T, Dombrovsky A, Elad Y, Berman S. Robotic disease detection in greenhouses: combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robotics and Automation Letters. 2016;1(1):354–60. https://doi.org/10.1109/LRA.2016.2518214
  74. 72. Jayas DS, Paliwal J, Erkinbaev C, Ghosh PK, Karunakaran C. Wheat quality evaluation. In: Sun DW, editor. Computer Vision Technology for Food Quality Evaluation. Academic Press; 2016. p. 385–412. https://doi.org/10.1016/c2014-0-01718-2
  75. 73. Mahlein AK. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease. 2016;100(2):241–51. https://doi.org/10.1094/PDIS-03-15-0340-FE
  76. 74. Chen C, Liang Y, Zhou L, Tang X, Dai M. An automatic inspection system for pest detection in granaries using YOLOv4. Computers and Electronics in Agriculture. 2022;201:107302. https://doi.org/10.1016/j.compag.2022.107302
  77. 75. Fountas S, Mylonas N, Malounas I, Rodias E, Hellmann Santos C, Pekkeriet E. Agricultural robotics for field operations. Sensors. 2020;20(9):2672. https://doi.org/10.3390/s20092672
  78. 76. Kumar A, Guleria A. Revolutionizing agriculture: The application of computer vision and drone technology. In: Chouhan SS, Singh UP, Jain S, editors. Applications of Computer Vision and Drone Technology in Agriculture 4.0 Singapore: Springer; 2024. p. 31–47. https://doi.org/10.1007/978-981-99-8684-2_17
  79. 77. Quan L, Jiang W, Li H, Li H, Wang Q, Chen L. Intelligent intra-row robotic weeding system combining deep learning technology with a targeted weeding mode. Biosystems Engineering. 2022;216:13–31. https://doi.org/10.1016/j.biosystemseng.2022.01.019
  80. 78. Andreasen C, Vlassi E, Salehan N, Johannsen KS, Jensen SM. Laser weed seed control: Challenges and opportunities. Frontiers in Agronomy. 2024;6:1342372. https://doi.org/10.3389/fagro.2024.1342372
  81. 79. Allmendinger A, Spaeth M, Saile M, Peteinatos GG, Gerhards R. Precision chemical weed management strategies: A review and a design of a new CNN-based modular spot sprayer. Agronomy. 2022;12(7):1620. https://doi.org/10.3390/agronomy12071620
  82. 80. Hussain MI, Vieites‐Álvarez Y, Otero P, Prieto MA, Simal‐Gandara J, Reigosa MJ, et al. Weed pressure determines the chemical profile of wheat (Triticum aestivum L.) and its allelochemicals potential. Pest Management Science. 2022;78(4):1605–19. https://doi.org/10.1002/ps.6779
  83. 81. Wu B, Zhang M, Zeng H, Tian F, Potgieter AB, Qin X, et al. Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review. 2023;10(4):290. https://doi.org/10.1093/nsr/nwac290
  84. 82. Das S, Chapman S, Christopher J, Choudhury MR, Menzies NW, Apan A, et al. UAV-thermal imaging: A technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils–A case review on wheat. Remote Sensing Applications: Society and Environment. 2021;23:100583. https://doi.org/10.1016/j.rsase.2021.100583
  85. 83. Shockley JM, Dillon CR, Shearer SA. An economic feasibility assessment of autonomous field machinery in grain crop production. Precision Agriculture. 2019:1068–85.grain https://doi.org/10.1007/s11119-019-09638-w
  86. 84. Xu R, Li C. A review of high-throughput field phenotyping systems: Focusing on ground robots. Plant Phenomics. 2022. https://doi.org/10.34133/2022/9760269
  87. 85. Zhou X, Bi S. A survey of bio-inspired compliant legged robot designs. Bioinspiration & biomimetics. 2012;7(4):041001. https://doi.org/10.1088/1748-3182/7/4/041001
  88. 86. Stager A, Tanner HG, Sparks E. Design and construction of unmanned ground vehicles for sub-canopy plant phenotyping. In: Lorence A, Medina Jimenez K, editors. High-Throughput Plant Phenotyping: Methods and Protocols. New York: Humana, New York, NY. 2022. p. 191–211. https://doi.org/10.1007/978-1-0716-2537-8_16
  89. 87. Yuan H, Song M, Liu Y, Xie Q, Cao W, Zhu Y, et al. Field phenotyping monitoring systems for high-throughput: A survey of enabling technologies, equipment, and research challenges. Agronomy. 2023;13(11):2832 https://doi.org/10.3390/agronomy13112832
  90. 88. Hu Q, Fan Z, Zhang X, Sun N, Li X, Qiu Q. Robust localization and tracking control of high-clearance robot system servicing high-throughput wheat phenotyping. Computers and Electronics in Agriculture. 2025;229:109793. https://doi.org/10.1016/j.compag.2024.109793
  91. 89. Cozzolino D. The role of near-infrared sensors to measure water relationships in crops and plants. Applied Spectroscopy Reviews. 2017;52(10):837–49. https://doi.org/10.1080/05704928.2017.1331446
  92. 90. Ashfaq W, Brodie G, Fuentes S, Gupta D. Infrared thermal imaging and morpho-physiological indices used for wheat genotypes screening under drought and heat stress. Plants. 2022;11(23):3269.https://doi.org/10.3390/plants11233269
  93. 91. Morrison MJ, Gahagan AC, Lefebvre MB. Measuring canopy height in soybean and wheat using a low‐cost depth camera. The Plant Phenome Journal. 2021;4(1):e20019. https://doi.org/10.1002/ppj2.20019
  94. 92. Kaya C. Optimizing crop production with plant phenomics through high‐throughput phenotyping and AI in controlled environments. Food and Energy Security. 2025;14(1):e70050. https://doi.org/10.1002/fes3.70050
  95. 93. Bao Y, Gai J, Xiang L, Tang L. Field robotic systems for high-throughput plant phenotyping: A review and a case study. High-Throughput Crop Phenotyping. 2021:13–38 https://doi.org/10.1007/978-3-030-73734-4_2
  96. 94. Bechar A, Vigneault C. Agricultural robots for field operations: Concepts and components. Biosystems Engineering. 2016;149:94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014
  97. 95. R Shamshiri R, Weltzien C, Hameed IA, J Yule I, E Grift T, Balasundram SK, et al. Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering. 2018;11(4):1–14. http://dx.doi.org/10.25165/j.ijabe.20181104.4278
  98. 96. Vougioukas SG. Agricultural robotics. Annual Review of Control, Robotics, and Autonomous Systems. 2019;2(1):365–92. https://doi.org/10.1146/annurev-control-053018-023617
  99. 97. Hajjaj SS, Sahari KS. Review of agriculture robotics: Practicality and feasibility. In: 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). IEEE; 2016. p. 194–8. https://doi.org/10.1109/IRIS.2016.8066090

Downloads

Download data is not yet available.