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Plant Science Today (PST)

Review Articles

Vol. 11 No. sp4 (2024): Recent Advances in Agriculture by Young Minds - I

Sky to soil: Role of drones in maximizing agricultural crops yield

DOI
https://doi.org/10.14719/pst.5773
Submitted
13 October 2024
Published
31-12-2024

Abstract

As agricultural practices aim to boost productivity to meet global food demand, drones have emerged as transformative tools offering aerial intelligence to guide data-driven crop management. Drones are revolutionizing agriculture by their capacity to perform precise tasks related to crop health monitoring, pest and disease detection, spraying, mapping and other related activities. Comparing the advancement with traditional ground techniques such as manual crop scouting, ground-based sprayers and satellite imagery reveals many advantages regarding accessibility, precision, efficiency, safety and sustainability, besides increasing farm productivity. Drones also help to make well-informed decisions by delivering timely, high-quality agricultural data to increase yields. However, significant obstacles, such as battery life restrictions, expertise shortages and problems with data processing, are highlighted. The various applications of drones in optimizing agricultural outcomes, ranging from assessing crop vigour to assisting in pollination, are conferred. Additionally, there is a vast potential for drones to transform farming through precision agriculture, provided that proactive measures are taken to address the limitations. With remote sensing and autonomous capacities improving continually, drones can grant farmers unmatched awareness and control to reduce risks and extract the maximum productivity from finite land resources.

References

  1. Dutta G, Goswami P. Application of drone in agriculture: a review. International Journal of Chemical Studies. 2020;8(5):181-17. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
  2. Giles D, Billing R. Deployment and performance of a UAV for crop spraying. Chemical Engineering Transactions. 2015;44:307-12. https://doi.org/10.3303/CET1544052
  3. Librán-Embid F, Klaus F, Tscharntke T, Grass I. Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes-a systematic review. Science of the Total Environment. 2020;732:139204. https://doi.org/10.1016/j.scitotenv.2020.139204
  4. Khan N, Ray RL, Sargani GR, Ihtisham M, Khayyam M, Ismail S. Current progress and future prospects of agriculture technology: gateway to sustainable agriculture. Sustainability. 2021;13(9):4883. https://doi.org/10.3390/su13094883
  5. Doddamani A, Kouser S, Ramya V. Role of drones in modern agricultural applications. Curr J Appli Sci Technol. 2020;39(48):216-24. https://doi.org/10.9734/cjast/2020/v39i4831224
  6. Ahirwar S, Swarnkar R, Bhukya S, Namwade G. Application of drone in agriculture. Inter J Curr Microbiol Appl Sci. 2019;8(01):2500-05. https://doi.org/10.20546/ijcmas.2019.801.264
  7. Ghazali MHM, Azmin A, Rahiman W. Drone implementation in precision agriculture–a survey. International Journal of Emerging Technology and Advanced Engineering. 2022;12(4):67-77. https://doi.org/10.46338/ijetae0422_10
  8. Ouda S, Zohry AEH, Noreldin T, Zohry A, Ouda S. Crop rotation defeats pests and weeds. Crop Rotation: An Approach to Secure Future Food. 2018:77-88. https://doi.org/10.1007/978-3-030-05351-2_5
  9. Larkin RP, Honeycutt CW, Olanya OM, Halloran JM, He Z. Impacts of crop rotation and irrigation on soilborne diseases and soil microbial communities. Sustainable Potato Production: Global Case Studies. 2012:23-41. https://doi.org/10.1007/978-94-007-4104-1_2
  10. Wallander S. While crop rotations are common, cover crops remain rare. Amber Waves. 2013:21.
  11. Yadav O, Hossain F, Karjagi C, Kumar B, Zaidi P, Jat S, et al. Genetic improvement of maize in India: retrospect and prospects. Agricultural Research. 2015;4:325-38. https://doi.org/10.1007/s40003-015-0180-8 .
  12. Evenson RE, Gollin D. Crop variety improvement and its effect on productivity: the impact of international agricultural research: Cabi Publishing; 2003. https://doi.org/10.1079/9780851995496.0000 .
  13. Duvick DN. The contribution of breeding to yield advances in maize (Zea mays L.). Advances in Agronomy. 2005;86:83-145. https://doi.org/10.1016/S0065-2113(05)86002-X .
  14. Schmitz A, Moss CB. Mechanized agriculture: machine adoption, farm size and labor displacement; 2015.
  15. Stojic B, Casnji F, Poznic A. The role of the mechatronics in technological development of the contemporary agricultural tractors. Acta Technica Corviniensis-Bulletin of Engineering. 2011;4(1):59.
  16. Whetham EH. The mechanization of british farming, 1910–1945. Journal of Agricultural Economics. 1970;21(3):317-31. https://doi.org/10.1111/j.1477-9552.1970.tb01388.x .
  17. Nin Y, Diao P, Wang Q, Zhang Q, Zhao Z, Li Z. On-farm-produced organic amendments on maintaining and enhancing soil fertility and nitrogen availability in organic or low input agriculture. Organ Fert. 2016;2016:289-307 . https://dx.doi.org/10.5772/61454 .
  18. Biramo G. The role of integrated nutrient management system for improving crop yield and enhancing soil fertility under Small holder farmers in Sub-Saharan Africa: A review article. Mod Concepts Dev Agron. 2018;2:1-9.
  19. Schneekloth JP, Klocke NL, Davison DR, Payero JO. Furrow irrigation management with limited water. Applied Engineering in Agriculture. 2006;22(3):391-98. https://doi.org/10.13031/2013.20459 .
  20. Baghdadi A, Golzardi F, Hashemi M. The use of alternative irrigation and cropping systems in forage production may alleviate the water scarcity in semiarid regions. Journal of the Science of Food and Agriculture; 2023 . https://doi.org/10.1002/jsfa.12574 .
  21. Debangshi U. Drones-applications in agriculture. Chronicle of Bioresource Management. 2021;5(Sep, 3):115-20.
  22. Nunes EC. Employing drones in agriculture: an exploration of various drone types and key advantage. arXiv preprint arXiv:2307.04037. 2023:1– 8. https://doi.org/10.48550/arXiv.2307.04037 .
  23. Rathod PD, Shinde GU. Autonomous aerial system (UAV) for sustainable agriculture: A Review. International Journal of Environment and Climate Change. 2023;13(8):1343-55. https://doi.org/10.9734/ijecc/2023/v13i82080
  24. Ganesan T, Jayarajan N, Sureshkumar P. Review on the real-time implementation of iot-enabled uav in precision agriculture and the overview of collision avoidance strategies. Philippine Journal of Science. 2023;152(3). https://doi.org/10.56899/152.03.29 .
  25. Simelli I, Tsagaris A, editors. In: The Use of Unmanned Aerial Systems (UAS) in Agriculture. Haicta; 2015
  26. Zhang C, Valente J, Kooistra L, Guo L, Wang W. Opportunities of UAVs in orchard management. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences. 2019;42:673-80. https://doi.org/10.5194/isprs-archives-XLII-2-W13-673-2019 .
  27. Maddikunta PKR, Hakak S, Alazab M, Bhattacharya S, Gadekallu TR, Khan WZ, et al. Unmanned aerial vehicles in smart agriculture: Applications, requirements and challenges. IEEE Sensors Journal. 2021;21(16):17608-19. https://doi.org/10.1109/JSEN.2021.3049471 .
  28. San KT, Mun SJ, Choe YH, Chang YS. UAV delivery monitoring system. MATEC Web of Conferences; 2018: EDP Sciences. https://doi.org/10.1051/matecconf/201815104011 .
  29. Yang S, Yang X, Mo J. The application of unmanned aircraft systems to plant protection in China. Precision Agriculture. 2018;19:278-92. https://doi.org/10.1007/s11119-017-9516-7 .
  30. Huang Y, Thomson SJ, Hoffmann WC, Lan Y, Fritz BK. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering. 2013;6(3):1-10.
  31. Al-Arab M, Torres-Rua A, Ticlavilca A, Jensen A, McKee M. Use of high-resolution multispectral imagery from an unmanned aerial vehicle in precision agriculture. 2013 IEEE international geoscience and remote sensing symposium; 2013: IEEE. https://doi.org/10.1109/igarss.2013.6723419 .
  32. Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science. 2019;24(2):152-64. http://doi.org/10.1016/j.tplants.2018.11.007 .
  33. Klemas VV. Coastal and environmental remote sensing from unmanned aerial vehicles: An overview. Journal of Coastal Research. 2015;31(5):1260-67. https://doi.org/10.2112/JCOASTRES-D-15-00005.1 .
  34. Dang LM, Hassan SI, Suhyeon I, kumar Sangaiah A, Mehmood I, Rho S, et al. UAV based wilt detection system via convolutional neural networks. Sustainable Computing: Informatics and Systems. 2020;28:100250. https://doi.org/10.1016/j.suscom.2018.05.010 .
  35. Calderón R, Navas-Cortés JA, Lucena C, Zarco-Tejada PJ. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment. 2013;139:231-45. https://doi.org/10.1016/j.rse.2013.07.031 .
  36. Vardhan J, Swetha KS. Detection of healthy and diseased crops in drone captured images using Deep Learning. arXiv preprint arXiv:230513490. 2023. https://doi.org/10.48550/arXiv.2305.13490 .
  37. Miller IJ, Schieber B, De Bey Z, Benner E, Ortiz JD, Girdner J, et al. Analyzing crop health in vineyards through a multispectral imaging and drone system. 2020 Systems and Information Engineering Design Symposium; 2020: IEEE. https://doi.org/10.1109/sieds49339.2020.9106671 .
  38. Vijayakumar S, Madireddy H, Bhusarapu SC, Kumar R, Sundaram R. Drone application in rice cultivation: Experiences from ICAR-IIRR trails. Indian Farming. 2022;72(12):3-6.
  39. Hegarty-Craver M, Polly J, O'Neil M, Ujeneza N, Rineer J, Beach RH, et al. Remote crop mapping at scale: Using satellite imagery and UAV-acquired data as ground truth. Remote Sensing. 2020;12(12):1984. https://doi.org/10.3390/rs12121984 .
  40. Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote sensing. 2015;7(4):4026-47. https://doi.org/10.3390/rs70404026 .
  41. Yallappa D, Veerangouda M, Maski D, Palled V, Bheemanna M. Development and evaluation of drone mounted sprayer for pesticide applications to crops. 2017 IEEE global humanitarian technology conference; 2017: IEEE. https://doi.org/10.1109/GHTC.2017.8239330 .
  42. Torres-Rua A, Al Arab M, Hassan-Esfahani L, Jensen A, McKee M. Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience. 2015 IEEE conference on technologies for sustainability; 2015: IEEE. https://doi.org/10.1109/SusTech.2015.7314326 .
  43. Dileep M, Navaneeth A, Ullagaddi S, Danti A. A study and analysis on various types of agricultural drones and its applications. 2020 5 th International conference on research in computational intelligence and communication networks; 2020: IEEE . https://doi.org/10.1109/icrcicn50933.2020.9296195 .
  44. Amarasinghe A, Wijesuriya VB, Ganepola D, Jayaratne L. A swarm of crop spraying drones solution for optimizing safe pesticide usage in arable lands. Proceedings of the 17th Conference on Embedded Networked Sensor Systems; 2019. https://doi.org/10.1145/3356250.3361948 .
  45. Dampage U, Navodana M, Lakal U, Warusavitharana A, editors. Smart agricultural seeds spreading drone for soft soil paddy fields. 2020 IEEE International Conference on Computing, Power and Communication Technologies; 2020: IEEE. https://doi.org/10.1109/gucon48875.2020.9231124 .
  46. Emimi M, Khaleel M, Alkrash A. The current opportunities and challenges in drone technology. Int J Electr Eng and Sustain. 2023:74-89.
  47. Honkavaara E, Saari H, Kaivosoja J, Pölönen I, Hakala T, Litkey P, et al. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing. 2013;5(10):5006-39. https://doi.org/10.3390/rs5105006 .
  48. Raeva PL, Å edina J, Dlesk A. Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing. 2019;52(sup1):192-201. https://doi.org/10.1080/22797254.2018.1527661 .
  49. Barbedo JGA. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones. 2019;3(2):40. https://doi.org/10.3390/drones3020040 .
  50. Al-Juthery HW, Lahmod NR, Al-Taee RA. Intelligent, nano-fertilizers: A new technology for improvement nutrient use efficiency. IOP Conference Series: Earth and Environmental Science; 2021: IOP Publishing. https://doi.org/10.1088/1755-1315/735/1/012086 .
  51. Bah A, Balasundram S, Husni M. Sensor technologies for precision soil nutrient management and monitoring. American Journal of Agricultural and Biological Sciences. 2012;7(1):43-49. https://doi.org/10.3844/ajabssp.2012.43.49 .
  52. Hunt Jr ER, Daughtry CS. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International Journal of Remote Sensing. 2018;39(15-16):5345-76. https://doi.org/10.1080/01431161.2017.1410300 .
  53. Geipel J, Peteinatos G, Claupein W, Gerhards R. Enhancement of micro unmanned aerial vehicles for agricultural aerial sensor systems. In: Stafford JV, editors. Precision agriculture. Wageningen Academic Publishers, Wageningen; 2013. https://doi.org/10.3920/9789086867783_020 .
  54. Montilla-Pacheco AdJ, Pacheco-Gil HA, Pastrán-Calles FR, Rodríguez-Pincay IR. Pollination with drones: A successful response to the decline of entomophiles pollinators?. Scientia Agropecuaria. 2021;12(4): 509-16
  55. Chechetka SA, Yu Y, Tange M, Miyako E. Materially engineered artificial pollinators. Chem. 2017;2(2):224-39. http://dx.doi.org/10.1016/j.chempr.2017.01.008 .
  56. Jiyu L, Lan Y, Jianwei W, Shengde C, Cong H, Qi L, et al. Distribution law of rice pollen in the wind field of small UAV. International Journal of Agricultural and Biological Engineering. 2017;10(4):32-40. https://doi.org/10.25165/j.ijabe.20171004.3103 .
  57. Li J, Zhou Z, Lan Y, Hu L, Zang Y, Liu A, et al. Distribution of canopy wind field produced by rotor unmanned aerial vehicle pollination operation. Transactions of the Chinese Society of Agricultural Engineering. 2015;31(3):77-86. https://doi.org/10.3969/j.issn.1002-6819.2015.03.011 .
  58. Svatos K, Trowbridge G. Australian drone technology assisting a significant step in crop tolerance to heat and drought stress; 2018. Available from: https://apo.org.au/node/135781
  59. Wilsdon B. Problems of irrigation. Nature. 1930;125(3157):674-77. https://doi.org/10.1038/125674a0 .
  60. Daponte P, De Vito L, Glielmo L, Iannelli L, Liuzza D, Picariello F. A review on the use of drones for precision agriculture. IOP Conference Series: Earth and Environmental Science; 2019: IOP Publishing. https://doi.org/10.3233/nicsp230002
  61. Lahza H, Naveen Kumar K, Sreenivasa B, Shawly T, Alsheikhy AA, Hiremath AK, et al. Optimization of crop recommendations using novel machine learning techniques. Sustainability. 2023;15(11):8836. https://doi.org/10.3390/su15118836
  62. Malveaux C, Hall SG, Price R. Using drones in agriculture: unmanned aerial systems for agricultural remote sensing applications. American Society of Agricultural and Biological Engineers. 2014; 1-20. https://doi.org/10.13031/aim.20141911016
  63. Awais M, Li W, Cheema M, Zaman Q, Shaheen A, Aslam B, et al. UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: A meta-review. Inter J Environ Sci Technol. 2022:1-18. https://doi.org/10.1007/s13762-021-03801-5
  64. Muchiri G, Kimathi S, editors. A review of applications and potential applications of UAV. Proceedings of the 2016 Annual Conference on Sustainable Research and Innovation, 4 – 6 May 2016; p. 1-4
  65. Hristov G, Kinaneva D, Georgiev G, Zahariev P, Kyuchukov P. An overview of the use of unmanned aerial vehicles for precision agriculture. 2020 International Conference on Biomedical Innovations and Applications (BIA), Varna, Bulgaria; 2020, p. 137-140. https://doi.org/10.1109/BIA50171.2020.9244519 .
  66. Bhuvaneshwari C, Saranyadevi G, Vani R, Manjunathan A. Development of high yield farming using IoT based UAV. IOP Conference Series: Materials Science and Engineering; 2021: IOP Publishing. https://doi.org/10.1088/1757-899X/1055/1/012007 .
  67. Manickam S. A drone-based IoT Approach to agriculture automation and increase farm yield. Available at SSRN 3713675. 2020. https://doi.org/10.2139/ssrn.3713675
  68. Vanegas F, Bratanov D, Powell K, Weiss J, Gonzalez F. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors. 2018;18(1):260. https://doi.org/10.3390/s18010260 .
  69. Inoue Y. Satellite-and drone-based remote sensing of crops and soils for smart farming–a review. Soil Science and Plant Nutrition. 2020;66(6):798-810. https://doi.org/10.1080/00380768.2020.1738899 .
  70. Bharti M, Bharti S. Drone technology as a tool for improving agricultural productivity. Journal of Soil and Water Conservation. 2020;19(4):446-51.
  71. Unal I, Topakci M, editors. A review on using drones for precision farming applications. Proceedings of the 12th International Congress on Agricultural Mechanization and Energy, Cappadocia, Turkey; 2014.
  72. Hunter III JE, Gannon TW, Richardson RJ, Yelverton FH, Leon RG. Integration of remote?weed mapping and an autonomous spraying unmanned aerial vehicle for site?specific weed management. Pest Management Science. 2020;76(4):1386-92. https://doi.org/10.1002/ps.5651 .
  73. Van der Merwe D, Burchfield DR, Witt TD, Kevin PP, Ajay S. Drones in agriculture. Advances in Agronomy. 2020;162:1-30.
  74. Tsouros DC, Bibi S, Sarigiannidis PG. A review on UAV-based applications for precision agriculture. Information. 2019;10(11):349. https://doi.org/10.3390/info10110349 .
  75. Gago J, Estrany J, Estes L, Fernie AR, Alorda B, Brotman Y, et al. Nano and micro unmanned aerial vehicles (UAVs): a new grand challenge for precision agriculture?. Current Protocols in Plant Biology. 2020;5(1):e20103. https://doi.org/10.1002/cppb.20103 .
  76. Sharma M, Hema N, editors. Comparison of agricultural drones and challenges in implementation: a review. 2021 7th International Conference on Signal Processing and Communication (ICSC), Noida, India, 2021, p. 26-30. https://doi.org/10.1109/ICSC53193.2021.9673491 .
  77. Kestur R, Omkar S, Subhash S. Unmanned aerial system technologies for pesticide spraying. Innovative Pest Management Approaches for the 21st Century: Harnessing Automated Unmanned Technologies. 2020:47-60. https://doi.org/10.1007/978-981-15-0794-6_3 .
  78. Romeo J, Pajares G, Montalvo M, Guerrero J, Guijarro M, Ribeiro A. Crop row detection in maize fields inspired on the human visual perception. The Scientific World Journal. 2012;2012. https://doi.org/10.1100/2012/484390 .
  79. Zhang C, Kovacs JM. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture. 2012;13:693-712. https://doi.org/10.1007/s11119-012-9274-5 .
  80. Ayamga M, Tekinerdogan B, Kassahun A. Exploring the challenges posed by regulations for the use of drones in agriculture in the African context. Land. 2021;10(2):164. https://doi.org/10.3390/land10020164 .
  81. Thapa P. Potential of unmanned aerial vehicles for agriculture: A review. Review of Behavioral Aspect in Organizations and Society. 2021;3(1):1-8. https://doi.org/10.32770/rbaos.vol31-8
  82. Jain S, Bhujel S, Shrivastava U, Mohapatra A, Mishra G. Advancements in drone technology for fruit crop management: a comprehensive review. International Journal of Environment and Climate Change. 2023;13(11):4367-78. https://doi.org/10.9734/ijecc/2023/v13i113617 .
  83. Kovács E, Sulyok D, Csenki S, Felföldi J. Farmers' perception of the opportunites offered by drone technology in arable crop production. Journal of Agricultural Informatics. 2023;14(1). https://doi.org/10.17700/jai.2023.14.1.695 .
  84. Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S, et al. Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS One. 2016;11(7):e0159781. https://doi.org/10.1371/journal.pone.0159781 .
  85. Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. Recent advances on UAV and deep learning for early crop diseases identification: A short review. 2021 International Conference on Information Technology (ICIT), Amman, Jordan, 2021, p. 334-39. https://doi.org/10.1109/ICIT52682.2021.9491661 .
  86. Bhushan M, Negi A. Impact of UAVs in Agriculture. In: Goel N, Yadav R, editors. Handbook of research on machine learning-enabled IOT for smart applications across industries. Hershey, PA: IGI Global Scientific Publishing; 2023. p. 258–68 https://doi.org/10.4018/978-1-6684-8785-3.ch013 .
  87. Zhang Z, Zhu L. A review on unmanned aerial vehicle remote sensing: platforms, sensors, data processing methods and applications. Drones. 2023;7(6):398. https://doi.org/10.3390/drones7060398 .
  88. Rominiyi OL, Salau AO, Adaramola BA, Ogunlade MA, Olanibi TO, Akintoye F. Development of a precision agricultural based unmanned aerial vehicle for pest control. 2023 International conference on cyber management and engineering, Bangkok, Thailand, 2023, p. 439-43 . Available from: https://ieeexplore.ieee.org/document/10051014 org/
  89. Tahir MN, Lan Y, Zhang Y, Wenjiang H, Wang Y, Naqvi SMZA. Application of unmanned aerial vehicles in precision agriculture. In: Qamar Z, editors. Precision Agriculture. Elsevier; 2023. p. 55-70. https://doi.org/10.1016/B978-0-443-18953-1.00001-5 .
  90. Vijayalakshmi K, Al-Otaibi S, Arya L, Almaiah MA, Anithaashri T, Karthik SS, et al. Smart agricultural–industrial crop-monitoring system using unmanned aerial vehicle–internet of things classification techniques. Sustainability. 2023;15(14):11242. https://doi.org/10.3390/su151411242 .
  91. Lawrence ID, Vijayakumar R, Agnishwar J. Dynamic application of unmanned aerial vehicles for analyzing the growth of crops and weeds for precision agriculture. In: Gupta R, Jain A, Wang J, Bharti S, & Patel S, editor. Hershey, PA: IGI Global Scientific Publishing; 2023. p. 115-32. https://doi.org/10.4018/978-1-6684-8516-3.ch007 .

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