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Plant Science Today (PST)

Review Articles

Early Access

Application of agricultural drones in vegetable cultivation: A comprehensive review

DOI
https://doi.org/10.14719/pst.8156
Submitted
10 March 2025
Published
15-04-2025
Versions

Abstract

Drones are unmanned aerial vehicle used for aerial data collection and spraying. The application of Agricultural drones (Spraying drones) in vegetable cultivation had a significant impact on farming practices. Spraying drones equipped with cameras and sensors can capture high-resolution images of crops, allowing farmers to identify potential issues such as pest infestations, disease outbreaks and nutrient deficiencies. Farmer’s efficiency is increased in terms of using Agricultural drones. It includes cost-effectiveness, return on investment (ROI), labour savings and improvements in productivity. The use of Agricultural drones, assisting farmers in tackling current challenges and ensuring future food production. Agricultural Drones enable farmers to monitor large areas of vegetable crops fields quickly, saving time and helps the farmers to achieve sustainable and financially sound operations compared to traditional methods. This precision information minimizes resource wastage and ensures optimal conditions for crop growth and yield. It potentially reducing labour costs and increasing the efficiency. It is cost-effective provides timely data leads to improved yields and reduced costs associated with over-fertilization and pesticide use. Research has shown that Agricultural drones can increase productivity up to 30 %. A study conducted in the United States indicated that Agricultural drones save farmers an 15 % in operational costs, primarily through more precise resource allocation. While drones offer a futuristic approach to agriculture, their application in vegetable crop management is fraught with challenges. As the agricultural sector continues to evolve, it will be crucial to weigh these disadvantages against the potential benefits to determine the role of drones in the future of vegetable crop management.

References

  1. Padhiary M, Sethi LN, Kumar A. Enhancing hill farming efficiency using unmanned agricultural vehicles: a comprehensive review. Transactions of the Indian National Academy of Engineering. 2024;9(2):253–68. https://doi.org/10.1007/s41403-024-00458-7
  2. Pal D, Joshi S. AI, IoT and robotics in smart farming: current applications and future potentials. In: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India. IEEE; 2023. p. 1096–101. https://doi.org/10.1109/ICSCDS56580.2023.10105101
  3. Ali A, Niu G, Masabni J, Ferrante A, Cocetta G. Integrated nutrient management of fruits, vegetables, and crops through the use of biostimulants, soilless cultivation, and traditional and modern approaches – A mini review. Agriculture. 2024;14(8):1330. https://doi.org/10.3390/agriculture14081330
  4. Anand A, Trivedi NK, Gautam V, Tiwari RG, Winarsih D, Misra A. Applications of Internet of Things (IoT) in agriculture: the need and implementation. In: 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), Bandung, Indonesia. IEEE; 2022. p. 01–05. https://doi.org/10.1109/ICADEIS56544.2022.10037505
  5. Furquim MG, Nascimento AR, Costa JV, Ferreira ME, Corcioli G, Borges LC. Remotely Piloted Aircraft Systems with RGB camera to map commercial table tomato nurseries. Mercator (Fortaleza). 2023;22:e22001. https://doi.org/10.4215/rm2023.e22001i
  6. Chaudhari VM, Barot DC, Patel RJ, Masaye SS. Precision cultivation of vegetable crops to increase productivity: a review. Asian Research Journal of Agriculture. 2024;17(3):235–45. https://doi.org/10.9734/arja/2024/v17i3500
  7. Chen C, Zheng Z, Xu T, Guo S, Feng S, Yao W, et al. Yolo-based UAV technology: A review of the research and its applications. Drones. 2023;7(3):190. https://doi.org/10.3390/drones7030190
  8. Astray A, Roulier M, Angeletti B, Dron J, Dauphin CE, Ambrosi JP, et al. Concentrations and transportation of metal and organochlorine pollutants in vegetables and risk assessment of human exposure in rural, urban and industrial environments (Bouchés-du-Rhône, France). Environmental Science and Pollution Research. 2021;28:64253–67. https://doi.org/10.1007/s11356-021-14604-z
  9. Barcelos CO, Fagundes-Júnior LA, Mendes AL, Gandolfo DC, Brandão AS. Integration of payload sensors to enhance UAV-based spraying. Drones. 2024;8(9):490. https://doi.org/10.3390/drones8090490
  10. Abbas A, Zhang Z, Zheng H, Alami MM, Areefa 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
  11. Abrahams M, Sibanda M, Dube T, Chimonyo VG, Mabhaudhi T. A systematic review of UAV applications for mapping neglected and underutilised crop species' spatial distribution and health. Remote Sensing. 2023;15(19):4672. https://doi.org/10.3390/rs15194672
  12. Bhattacharyay D, Maitra S, Pine S, Shankar T, Pedda Ghouse Peera SK. Future of precision agriculture in India. Protected Cultivation and Smart Agriculture. 2020;1:289–99. https://doi.org/10.30954/NDP-PCSA.2020.32
  13. Bojarski B, Vaitekhovich I, Tanaka S, Genes D, Sato T, Hasegawa H. Comparative analysis of remote sensing via drone and on-the-go soil sensing via Veris U3: a dynamic approach. Environmental Sciences Proceedings. 2023;29(1):11. https://doi.org/10.3390/ECRS2023-15846
  14. Boruah T, Kalita M, Hasna S, Das KS, Singh R, Nayak GA. Role of digital technologies in the field of horticultural science and technology. In: Novel approach to sustainable temperate horticulture. CRC Press; 2024. p. 116–48. https://doi.org/10.1201/9781003412489-6
  15. Boursin's AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, et al. Internet of things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things. 2022;18:100187. https://doi.org/10.1016/j.iot.2020.100187
  16. Canicattì M, Vallone M. Drones in vegetable crops: A systematic literature review. Smart Agricultural Technology. 2024;7:100396. https://doi.org/10.1016/j.atech.2024.100396
  17. Carvalho FK, Chechetto RG, Mota AA, Antuniassi UR. Challenges of aircraft and drone spray applications. Outlooks on Pest Management. 2020;31(2):83–8. https://doi.org/10.1564/v31_apr_08
  18. Ahuja K, Arora S. Automated crop cultivation and pesticide scheduling: a case study. In: Agri 4.0 and the future of cyber-physical agricultural systems. Academic Press; 2024. p. 279–95. https://doi.org/10.1016/B978-0-443-13185-1.00015-0
  19. Akhter A, Nabi A, Narayan S, Akhter S, Lone BA, Yousuf V, et al. Digital technology: a game changer in vegetable cultivation. Annual Research and Review in Biology. 2024;39(2):30–52. https://doi.org/10.9734/arrb/2024/v39i230631
  20. Chandra H, Nidamanuri RR. Object-based spectral library for knowledge-transfer-based crop detection in drone-based hyperspectral imagery. Precision Agriculture. 2025;26(1):1–22. https://doi.org/10.1007/s11119-024-10203-3
  21. Choudhary VK. Applications of emerging smart technologies in farming systems: a review. Global Journal of Computer Science and Technology. 2023;23:49–64. https://doi.org/10.34257/GJCSTGVOL23IS1PG49
  22. Desyatnyuk O, Muravskyi V, Shevchuk O. Accounting automation in agro-industrial enterprises using drones (UAVs). In: 2021 11th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany. IEEE; 2021. p. 337–341. https://doi.org/10.1109/ACIT52158.2021.9548424
  23. El Hoummaidi L, Larabi A, Alam K. Using unmanned aerial systems and deep learning for agriculture mapping in Dubai. Heliyon. 2021;7(10):e08154. https://doi.org/10.1016/j.heliyon.2021.e08154
  24. Farooq MS, Riaz S, Helou MA, Khan FS, Abid A, Alvi A. Internet of things in greenhouse agriculture: a survey on enabling technologies, applications, and protocols. IEEE. 2022;10:53374–97. https://doi.org/10.1109/ACCESS.2022.3166634
  25. Gokool S, Mahomed M, Clulow A, Sibanda M, Kunz R, Naiken V, et al. Exploring the potential of remote sensing to facilitate integrated weed management in smallholder farms: A scoping review. Drones. 2024;8(3):81. https://doi.org/10.3390/drones8030081
  26. Gupta R, Kataria D, Tripathi BS. Drone harvester: Detect and collect ripen fruit and vegetables. In: Applying drone technologies and robotics for agricultural sustainability. IGI Global; 2023. p. 108–123. https://doi.org/10.4018/978-1-6684-6413-7.ch007
  27. Hiraguri T, Shimizu H, Kimura T, Matsuda T, Maruta K, Takemura Y, et al. Autonomous drone-based pollination system using AI classifier to replace bees for greenhouse tomato cultivation. IEEE Access. 2023;11:99352–64. https://doi.org/10.1109/ACCESS.2023.3312151
  28. Go SH, Lee DH, Na SI, Park JH. Analysis of growth characteristics of kimchi cabbage using drone-based cabbage surface model image. Agriculture. 2022;12(2):216. https://doi.org/10.3390/agriculture12020216
  29. Go SH, Park JH. The early prediction of kimchi cabbage heights using drone imagery and the Long Short-Term Memory (LSTM) model. Drones. 2024;8(9):499. https://doi.org/10.3390/drones8090499
  30. Del Cerro J, Cruz Ulloa C, Barrientos A, de León Rivas J. Unmanned aerial vehicles in agriculture: A survey. Agronomy. 2021;11(2):203. https://doi.org/10.3390/agronomy11020203
  31. Dutta PK, Mitra S. Application of agricultural drones and IoT to understand food supply chain during post COVID?19. In: Choudhury A, Biswas A, Prateek M, Chakrabarti A, editors. Agricultural informatics: Automation Using the IoT and Machine Learning. Wiley; 2021. p. 67–87. https://doi.org/10.1002/9781119769231.ch4
  32. Hu J, Lu H, Song K, Zhu B. Vegetable fields mapping in northeast china based on phenological features. Agronomy. 2025;15(2):307. https://doi.org/10.3390/agronomy15020307
  33. Jafar A, Bibi N, Naqvi RA, Sadeghi-Niaraki A, Jeong D. Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Frontiers in Plant Science. 2024;15:1356260. https://doi.org/10.3389/fpls.2024.1356260
  34. Jain M, Bajwa MS, Kumar H. Agriculture assistant for crop prediction and farming selection using machine learning model with real-time data using imaging through UAV drone. In: Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021. Singapore: Springer; 2022. p. 311–30. https://doi.org/10.1007/978-981-16-8774-7_26
  35. Jasim AN, Fourati LC. Agriculture 4.0 from IoT, Artificial Intelligence, Drone, & Blockchain perspectives. In: 2023 15th International Conference on Developments in eSystems Engineering. IEEE; 2023. p. 262–7. https://doi.org/10.1109/DeSE58274.2023.10099927
  36. Jayalath MM, Perera AN, Ratnayake RC, Thibbotuwawa A. Towards digital transformation of vegetable supply chains in developing economies. In: 2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE; 2024. p. 359–64. https://doi.org/10.1109/ECTIDAMTNCON60518.2024.10480012
  37. Kamilaris A, Gao F, Prenafeta-Boldu FX, Ali MI. Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). IEEE; 2016. p. 442–7. https://doi.org/10.1109/WF-IoT.2016.7845467
  38. Ibiev GZ, Savoskina OA, Chebanenko SI, Beloshapkina OO, Zavertkin IA. Unmanned aerial vehicles (UAVs)-One of the digitalization and effective development segments of agricultural production in modern conditions. In: AIP Conference Proceedings. Vol. 2661, No. 1. AIP Publishing; 2022. https://doi.org/10.1063/5.0107373
  39. Incrocci L, Massa D, Pardossi A. New trends in the fertigation management of irrigated vegetable crops. Horticulturae. 2017;3(2):37. https://doi.org/10.3390/horticulturae3020037
  40. Ivezi? A, Trudi? B, Stamenkovi? Z, Kuzmanovi? B, Peri? S, Ivoševi? B, et al. Drone-related agrotechnologies for precise plant protection in western balkans: Applications, possibilities, and legal framework limitations. Agronomy. 2023;13(10):2615. https://doi.org/10.3390/agronomy13102615
  41. Karimzadeh R, Tabatabaie E, Hejazi MJ, Behmaram S. Using drone for chemical control of cabbage aphid, Brevicone brassicae L. (Hemiptera: Aphididae) in canola fields. Journal of Entomological Society of Iran. 2025;45(1):75–85. https://doi.org/10.61186/jesi.45.1.6
  42. Kaushik K. Smart agriculture applications using cloud and IoT. In: Rawat DB, Awasthi LK, Balas VE, Mohit Kumar, Jitendra Kumar S, editors. Convergence of cloud with AI for big data analytics: Foundations and innovation. Wiley; 2023. p. 89–105. https://doi.org/10.1002/9781119905233.ch5
  43. Kazi S, Jahangir A. Blockchain based agriculture using the application of UAV and deep learning technique: Alexnet CNN. Malaysian Journal of Science and Advanced Technology. 2023;3(2):91–100. https://doi.org/10.56532/mjsat.v3i2.147
  44. Khang A, editor. Handbook of research on AI-equipped IoT applications in high-tech agriculture. IGI Global; 2023. https://doi.org/10.4018/978-1-6684-9231-4
  45. Kim D, Cho W, Na I, Na MH. Prediction of live bulb weight for field vegetables using functional regression models and machine learning methods. Agriculture. 2024;14(5):754. https://doi.org/10.3390/agriculture14050754
  46. Kumar A, Rajput R, Bihari C, Kumari S, Rahman A, Kanaujia SP, et al. Role of artificial intelligence in vegetable production: A review. Journal of Scientific Research and Reports. 2024;30(9):950–63. https://doi.org/10.9734/jsrr/2024/v30i92423
  47. Kumar K A, Verma S. Harnessing computer vision for agricultural transformation: insights, techniques, and applications. In: Applications of computer vision and drone technology in Agriculture 4.0. Singapore: Springer; 2024. p. 111–31. https://doi.org/10.1007/978-981-99-8684-2_8
  48. Lee DH, Shin HS, Park JH. Identification of precision vegetation variations of Chinese cabbage using UAV and sensors. In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE; 2019. p. 7314–7. https://doi.org/10.1109/IGARSS.2019.8899801
  49. 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
  50. Mahasneh H. Drones in agriculture: Real-world applications and impactful case studies. Journal of Natural Science Review. 2024;2:643–56. https://doi.org/10.62810/jnsr.v2iSpecial.Issue.164
  51. Marcone A, Impollonia G, Croci M, Blandinières H, Pellegrini N, Amaducci S. Garlic yield monitoring using vegetation indices and texture features derived from UAV multispectral imagery. Smart Agricultural Technology. 2024;8:100513. https://doi.org/10.1016/j.atech.2024.100513
  52. Muralidharan C, Yoosuf MS, Rajkumar Y, Shivaprasad DD. Internet of agro drones for precision agriculture. In: Internet of drones. CRC Press; 2023. p. 139–53. https://doi.org/10.1201/9781003252085-9
  53. Norasma CY, Fadzilah MA, Roslin NA, Zanariah ZW, Tarmidi Z, Candra FS. Unmanned aerial vehicle applications in agriculture. In: IOP Conference Series: Materials Science and Engineering. Vol. 506. IOP Publishing; 2019. p. 012063. https://doi.org/10.1088/1757-899X/506/1/012063
  54. Akdo?an C, Özer T, O?uz Y. Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques. Robotic Intelligence and Automation. 2024;44(1):131–51. https://doi.org/10.1108/RIA-05-2023-0068
  55. Pal H, Tripathi S. Design IoT-Based smart agriculture to reduce vegetable waste by computer vision and machine learning. In: International Conference on Communications and Cyber Physical Engineering 2018. Singapore: Springer; 2023. p. 607–21. https://doi.org/10.1007/978-981-19-8086-2_59
  56. Pallottino F, Pane C, Figorilli S, Pentangelo A, Antonucci F, Costa C. Greenhouse application of light-drone imaging technology for assessing weeds severity occurring on baby-leaf red lettuce beds approaching fresh-cutting. Spanish Journal of Agricultural Research. 2020;18(3):7. https://doi.org/10.5424/sjar/2020183-15232
  57. Peppes N. The role of drones as an enabler for the 4th agricultural revolution. Current Research in Agricultural Sciences. 2020;7(2):40–51. https://doi.org/10.18488/journal.68.2020.72.40.51
  58. Anand R. Drone spraying system for efficient agrochemical application in precision agriculture. In: Applications of Computer Vision and Drone Technology in Agriculture 4.0. Singapore: Springer; 2024. p. 225–44. https://doi.org/10.1007/978-981-99-8684-2_13
  59. Messina G, Pratico S, Badagliacca G, Di Fazio S, Monti M, Modica G. Monitoring onion crop “Cipolla rossa di Tropea Calabria IGP” growth and yield response to varying nitrogen fertilizer application rates using UAV imagery. Drones. 2021;5(3):61. https://doi.org/10.3390/drones5030061
  60. Moradi S, Bokani A, Hassan J. UAV-based smart agriculture: A review of UAV sensing and applications. In: 2022 32nd International Telecommunication Networks and Applications Conference (ITNAC). IEEE; 2022. p. 181–4. https://doi.org/10.1109/ITNAC55475.2022.9998411
  61. Sarma AS, Nidamanuri RR. Active learning-enhanced plant-level crop mapping with drone hyperspectral imaging and evolutionary computing. In: 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE; 2023. p. 1–5. https://doi.org/10.1109/WHISPERS61460.2023.10430799
  62. Schaefer L. An emerging era of artificial intelligence research in agriculture. Journal of Robotics Spectrum. 2023;1:36–46. https://doi.org/10.53759/9852/JRS202301004
  63. Serrano T, Brym ZT, Monserrate LA, Her YG, Stanford J, Bhadha JH, et al. Nitrogen fertilizer effects on hemp biomass production detected by drone-based spectral imaging. HortScience. 2025;60(3):353–61. https://doi.org/10.21273/HORTSCI18264-24
  64. Shah SA, Lakho GM, Keerio HA, Sattar MN, Hussain G, Mehdi M, et al. Application of drone surveillance for advance agriculture monitoring by android application using convolution neural network. Agronomy. 2023;13(7):1764. https://doi.org/10.3390/agronomy13071764
  65. Shamshiri RR, Rad AK, Behjati M, Balasundram SK. Sensing and perception in robotic weeding: innovations and limitations for digital agriculture. Sensors. 2024;24(20):6743. https://doi.org/10.3390/s24206743
  66. Shankar RH, Veeraraghavan AK, Sivaraman K, Ramachandran SS. Application of UAV for pest, weeds and disease detection using open computer vision. In: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE; 2018. p. 287–92. https://doi.org/10.1109/ICSSIT.2018.8748404
  67. Singh R, Singh R, Gehlot A, Akram SV, Priyadarshi N, Twala B. Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming. Applied Sciences. 2022;12(24):12557. https://doi.org/10.3390/app122412557
  68. Singh T, Bhadwaj H, Verma L, Navadia NR, Singh D, Sakalle A, et al. Applications of AI in Agriculture. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. 2022:181–203. https://doi.org/10.2174/9789815036060122010011
  69. Srivastava A, Jain S, Maity R, Desai VR. Demystifying artificial intelligence amidst sustainable agricultural water management. Current Directions in Water Scarcity Research. 2022;7:17–35. https://doi.org/10.1016/B978-0-323-91910-4.00002-9
  70. Subeesh A, Mehta CR. Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture. 2021;5:278–91. https://doi.org/10.1016/j.aiia.2021.11.004
  71. Subramanian KS, Pazhanivelan S, Srinivasan G, Santhi R, Sathiah N. Drones in insect pest management. Frontiers in Agronomy. 2021;3:640885. https://doi.org/10.3389/fagro.2021.640885
  72. Takata Y, Yamada H, Kanuma N, Ise Y, Kanda T. Digital soil mapping using drone images and machine learning at the sloping vegetable fields in cool highland in the Northern Kanto region, Japan. Soil Science and Plant Nutrition. 2023;69(4):221–30. https://doi.org/10.1080/00380768.2023.2197453
  73. Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. 2020;4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002
  74. Veloo K, Kojima H, Takata S, Nakamura M, Nakajo H. Interactive cultivation system for the future IoT-based agriculture. In: 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW). IEEE; 2019. p. 298–304. https://doi.org/10.1109/CANDARW.2019.00059
  75. Velusamy P, Rajendran S, John William AD. Machine vision in UAV data analytics for precision agriculture. In: Drone Data Analytics in Aerial Computing. Singapore: Springer; 2023. p. 145–62. https://doi.org/10.1007/978-981-99-5056-0_8
  76. Vimal V, Savita. Sustainable production of underutilized vegetables. In: Production technology of underutilized vegetable crops. Springer, Cham; 2023. p. 369–87. https://doi.org/10.1007/978-3-031-15385-3_20
  77. Wang H, He Y, Zhang W, Liao J, Zheng Q. Integration of cultivation techniques and innovation of production models for specialty vegetable: broccoli. Journal of Modern Business and Economics. 2024;1(3). https://doi.org/10.70767/jmbe.v1i3.421
  78. Wang L, Huang X, Li W, Yan K, Han Y, Zhang Y, Pawlowski L, Lan Y. Progress in Agricultural Unmanned Aerial Vehicles (UAVs) applied in China and prospects for Poland. Agriculture. 2022;12(3):397. https://doi.org/10.3390/agriculture12030397
  79. Yadav A, Devi KM, Panme FA, Kumar J. Applications of AI and IoT technology in protected cultivation for enhancing agricultural productivity: A concise review. In: AI to Improve e-Governance and Eminence of Life: Kalyanathon 2020. Singapore: Springer; 2023. p. 37–57. https://doi.org/10.1007/978-981-99-4677-8_3
  80. Zhang J, Yu F, Zhang Q, Wang M, Yu J, Tan Y. Advancements of UAV and deep learning technologies for weed management in Farmland. Agronomy. 2024;14(3):494. https://doi.org/10.3390/agronomy14030494
  81. Rahman MF, Fan S, Zhang Y, Chen L. A comparative study on application of unmanned aerial vehicle systems in agriculture. Agriculture. 2021;11(1):22. https://doi.org/10.3390/agriculture11010022
  82. Petrovic B, Kononets Y, Csambalik L. Adoption of drone, sensor, and robotic technologies in organic farming systems of Visegrad countries. Heliyon. 2025;11(1):e41408. https://doi.org/10.1016/j.heliyon.2024.e41408
  83. Psiroukis V, Papadopoulos G, Darra N, Koutsiaras MG, Lomis A, Kasimati A, et al. Unmanned aerial vehicles applications in vegetables and arable crops. In: Unmanned aerial systems in agriculture. Academic Press; 2023. p. 71–91. https://doi.org/10.1016/B978-0-323-91940-1.00004-9

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