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مروری بر پتانسیل و مشکلات استفاده از فناوری هواپیماهای بدون سرنشین در کشاورزی پایدار

مقالات آماده انتشار

نوع مقاله : مقاله مروری انگلیسی

نویسندگان

1 گروه سنجش از دور و GIS، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند

2 مرکز نانوفناوری کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند

3 گروه برنج، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند

4 گروه علوم خاک و شیمی کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند

5 گروه مهندسی عمران، دانشکده مهندسی و فناوری دولتی آلاگاپا چتیار، کارائیکودی، تامیل نادو، هند

چکیده

هواپیماهای بدون سرنشین (پهباد) به‌عنوان یک فناوری با پتانسیل بالا در کشاورزی دقیق ظهور کرده‌اند و از اهداف توسعه پایدار (SDGs) با تقویت شیوه‌های کشاورزی پایدار، بهبود امنیت غذایی و کاهش اثرات زیست‌محیطی حمایت می‌کنند. این مقاله مروری بر تحلیل دقیق کاربردهای چندگانه فناوری هواپیماهای بدون سرنشین در کشاورزی، مانند نظارت بر سلامت محصول، پاشش آفت‌کش و کود، کنترل علف‌های هرز و تصمیم‌گیری مبتنی بر داده‌ها برای بهینه‌سازی مزرعه در نظر گرفته شده است. این مقاله بر نقش پهپادها در سمپاشی دقیق، ترویج مداخلات هدفمند و به حداقل رساندن اثرات زیست‌محیطی در مقایسه با روش‌های مرسوم تاکید دارد. هواپیماهای بدون سرنشین نقش حیاتی در مدیریت علف‌های هرز و ارزیابی سلامت محصول دارند. تمرکز این مقاله بر اهمیت داده‌های جمع‌آوری‌شده توسط هواپیماهای بدون سرنشین برای به‌دست آوردن اطلاعات لازم برای تصمیم‌گیری در مورد آبیاری، کوددهی و مدیریت کلی مزرعه است. با این حال، استفاده از وسایل نقلیه هوایی بدون سرنشین (پهپاد) در کشاورزی با چالش‌های ناشی از عمر باتری‌ها، زمان محدود پرواز و مشکلات اتصال، به‌ویژه در مناطق دورافتاده، مواجه است. چالش‌های قانونی، چارچوب‌های نظارتی و محدودیت‌هایی در مناطق مختلف نیز وجود دارد که بر عملکرد هواپیماهای بدون سرنشین تأثیر می‌گذارند. با تحقیق و توسعه مستمر، چالش‌های ارائه‌شده را می‌توان حل کرد و از حداکثر پتانسیل پهپادها برای دستیابی به کشاورزی پایدار استفاده کرد.

کلیدواژه‌ها

موضوعات

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

مراجع
  1. Ajakaiye, O. B. (2023). Drone Agricultural Technology: Implications for Sustainable Food Production in Africa. African Journal of Agricultural Science and Food Research, 9(1), 36-44. Retrieved from https://publications.afropolitanjournals.com/index.php/ajasfr/article/view/397
  2. Ali, M., Al-Ani, A., Eamus, D., & Tan, D. K. (2017). Leaf nitrogen determination using non-destructive techniques–A review. Journal of Plant Nutrition, 40(7), 928-953. https://doi.org/10.1080/01904167.2016.1143954
  3. Alkhamis, W. (2021). Emirati brothers develop drone that can pollinate a date palm tree in less than a minute. The National. Retrieved from: https://www.thenationalnews.com/uae/2021/11/14/emirati-brothers-develop-drone-that-can-pollinate-a-date-palm-tree-in-less-than-a-minute
  4. Altawy, R., & Youssef, A. M. (2016). Security, privacy, and safety aspects of civilian drones: A survey. ACM Transactions on Cyber-Physical Systems, 1(2), 1-25. https://doi.org/10.1145/3001836
  5. Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., & Gültekin, S. S. (2022). A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3), 1047. https://doi.org/10.3390/app12031047
  6. Balasubramaniam, P., & Ananthi, V. (2016). Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm. Nonlinear Dynamics, 83, 849-866. https://doi.org/10.1007/s11071-015-2372-y
  7. Baradaran Motie, J., Saeidirad, M. H. & Jafarian, M. (2023). Identification of Sunn-pest affected (Eurygaster Integriceps put.) wheat plants and their distribution in wheat fields using aerial imaging. Ecological Informatics, 76, p.102146. https://doi.org/10.1016/j.ecoinf.2023.102146
  8. Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40. https://doi.org/10.3390/drones3020040
  9. Barbedo, J. G. A., & Koenigkan, L. V. (2018). Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on agriculture, 47(3), 214-222. https://doi.org/10.1177/0030727018781876
  10. Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on geoscience and Remote Sensing, 47(3), 722-738. https://doi.org/10.1109/TGRS.2008.2010457
  11. Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359-387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
  12. Broussard, M. A., Coates, M., & Martinsen, P. (2023). Artificial pollination technologies: A review. Agronomy, 13(5), 1351. https://doi.org/10.3390/agronomy13051351
  13. Calderón Madrid, R., Navas Cortés, J. A., Lucena León, C., & Zarco-Tejada, P. J. (2013). High-resolution hyperspectral and thermal imagery acquired from UAV platforms for early detection of Verticillium wilt using fluorescence, temperature and narrow-band indices. Remote Sensing of Environment, 139, 231-245. https://doi.org/10.1016/j.rse.2013.07.031
  14. Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., & Suomalainen, J. (2015). Estimating plant traits of grasslands from UAV-acquired hyperspectral images: a comparison of statistical approaches. ISPRS International Journal of Geo-Information, 4(4), 2792-2820. https://doi.org/10.3390/ijgi4042792
  15. Chapman, S. C., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M. F., Holland, E., Zheng, B., Ling, T. J., & Jimenez-Berni, J. (2014). Pheno-copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy, 4(2), 279-301. https://doi.org/10.3390/agronomy4020279
  16. Chen, C. J., Huang, Y. Y., Li, Y. S., Chen, Y. C., Chang, C. Y., & Huang, Y. M. (2021). Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986-21997. https://doi.org/10.1109/ACCESS.2021.3056082
  17. Chen, P., Ouyang, F., Wang, G., Qi, H., Xu, W., Yang, W., Zhang, Y., & Lan, Y. (2021). Droplet distributions in cotton harvest aid applications vary with the interactions among the unmanned aerial vehicle spraying parameters. Industrial Crops and Products, 163, 113324. https://doi.org/10.1016/j.indcrop.2021.113324
  18. Christiansen, M. P., Laursen, M. S., Jørgensen, R. N., Skovsen, S., & Gislum, R. (2017). Designing and testing a UAV mapping system for agricultural field surveying. Sensors, 17(12), 2703. https://doi.org/10.3390/s17122703
  19. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
  20. Dampage, U., Navodana, M. D. R., Lakal, U. G. S., & Warusavitharana, A. M. (2020, October). Smart agricultural seeds spreading drone for soft soil paddy fields. In 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 373-377). https://doi.org/10.1109/GUCON48875.2020.9231124
  21. D'Sa, R., Jenson, D., Henderson, T., Kilian, J., Schulz, B., Calvert, M., Heller, T., & Papanikolopoulos, N. (2016). SUAV: Q-An improved design for a transformable solar-powered UAV. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/IROS.2016.7759260
  22. Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., & Dungey, H. S. (2017). Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 1-14. https://doi.org/10.1016/j.isprsjprs.2017.07.007
  23. Dayana, K., Ramesh, T., Avudaithai, S., Sebastian, P., & Selvaraj, R. (2022). Feasibility of using drone for foliar spraying of nutrients in irrigated green gram (Vigna radiata). Ecology, Environment and Conservation, 28, 64-64.
  24. Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., & Sun, X. (2021). A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sensing, 13(6), 1204. https://doi.org/10.3390/rs13061204
  25. Dezordi, L. R., Aquino, L. A. d., Aquino, R. F. B. d. A., Clemente, J. M., & Assunção, N. S. (2016). Diagnostic methods to assess the nutritional status of the carrot crop. Revista Brasileira de Ciência do Solo, 40. https://doi.org/10.1590/18069657rbcs20140813
  26. Dhillon, R., & Moncur, Q. (2023). Small-scale farming: A review of challenges and potential opportunities offered by technological advancements. Sustainability, 15(21), 15478. https://doi.org/10.3390/su152115478
  27. Dileep, M., Navaneeth, A., Ullagaddi, S., & Danti, A. (2020). A study and analysis on various types of agricultural drones and its applications. 2020 fifth international conference on research in computational intelligence and communication networks (ICRCICN), https://doi.org/10.1109/ICRCICN50933.2020.9296195
  28. Dündar, Ö., Bilici, M., & Ünler, T. (2020). Design and performance analyses of a fixed wing battery VTOL UAV. Engineering Science and Technology, an International Journal, 23(5), 1182-1193. https://doi.org/10.1016/j.jestch.2020.02.002
  29. Dutta, G., & Goswami, P. (2020). Application of drone in agriculture: A review. International Journal of Chemical Studies, 8(5), 181-187. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
  30. Dutta, S., Singh, A. K., Mondal, B. P., Paul, D., & Patra, K. (2023). Digital Inclusion of the Farming Sector Using Drone Technology. https://doi.org/10.5772/intechopen.108740
  31. Elmeseiry, N., Alshaer, N., & Ismail, T. (2021). A detailed survey and future directions of unmanned aerial vehicles (uavs) with potential applications. Aerospace, 8(12), 363. https://doi.org/10.3390/aerospace8120363
  32. Elouarouar, S., & Medromi, H. (2022). Multi-rotors unmanned aerial vehicles power supply and energy management. In E3S web of conferences (Vol. 336, p. 00068). EDP Sciences. https://doi.org/10.1051/e3sconf/202233600068
  33. Emimi, M., Khaleel, M., & Alkrash, A. (2023). The current opportunities and challenges in drone technology. International Journal of Electrical Engineering and Sustainability, 74-89. https://ijees.org/index.php/ijees/article/view/47
  34. Erdogan, P. (2023). The Importance of Agricultural Aviation in Plant Protection. Current Topics in Aeronautics, 127.
  35. Ennouri, K., & Kallel, A. (2019). Remote sensing: an advanced technique for crop condition assessment. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/9404565
  36. Freeman, P. K., & Freeland, R. S. (2015). Agricultural UAVs in the US: potential, policy, and hype. Remote Sensing Applications: Society and Environment, 2, 35-43. https://doi.org/10.1016/j.rsase.2015.10.002
  37. Ferraz, M. A. J., Santiago, A. G. D. S. G., Bruzi, A. T., & Vilela, N. J. D. (2024). Defoliation Categorization in Soybean with Machine Learning Algorithms and UAV Multispectral Data. https://doi.org/10.3390/agriculture14112088
  38. Frouin, R. J., Franz, B. A., Ibrahim, A., Knobelspiesse, K., Ahmad, Z., Cairns, B., Chowdhary, J., Dierssen, H. M., Tan, J., & Dubovik, O. (2019). Atmospheric correction of satellite ocean-color imagery during the PACE era. Frontiers in Earth Science, 7, 145. https://doi.org/10.3389/feart.2019.00145
  39. Gabriel, J. L., Zarco-Tejada, P. J., López-Herrera, P. J., Pérez-Martín, E., Alonso-Ayuso, M., & Quemada, M. (2017). Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering, 160, 124-133. https://doi.org/10.1016/j.biosystemseng.2017.06.003
  40. García-Munguía, A., Guerra-Ávila, P. L., Islas-Ojeda, E., Flores-Sánchez, J. L., Vázquez-Martínez, O., García-Munguía, A. M., & García-Munguía, O. (2024). A Review of Drone Technology and Operation Processes in Agricultural Crop Spraying. Drones, 8(11), 674. https://doi.org/10.3390/drones8110674
  41. Garg, P. K. (2022). Characterisation of Fixed-Wing Versus Multirotors UAVs/Drones. Journal of Geomatics, 16(2), 152-159. https://doi.org/10.58825/jog.2022.16.2.44
  42. Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies, 26(6), 6923-6947. https://doi.org/10.1007/s10639-021-10523-1
  43. Guzman, L. M., Chamberlain, S. A., & Elle, E. (2021). Network robustness and structure depend on the phenological characteristics of plants and pollinators. Ecology and Evolution, 11(19), 13321-13334. https://doi.org/10.1002/ece3.8055
  44. Hafeez, A., Husain, M. A., Singh, S., Chauhan, A., Khan, M. T., Kumar, N., Chauhan, A., & Soni, S. (2022). Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture, 10(2), 192-203. https://doi.org/10.1016/j.inpa.2022.02.002
  45. Hardin, P. J., & Jensen, R. R. (2011). Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities. GIScience & Remote Sensing, 48(1), 99-111. https://doi.org/10.2747/1548-1603.48.1.99
  46. Herwitz, S., Johnson, L., Dunagan, S., Higgins, R., Sullivan, D., Zheng, J., Lobitz, B., Leung, J., Gallmeyer, B., & Aoyagi, M. (2004). Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44(1), 49-61. https://doi.org/10.1016/j.compag.2004.02.006
  47. Hosseini, S. A., Masoudi, H., Sajadiye, S. M., & Abdanan Mehdizadeh, S. (2021). Nitrogen Estimation in Sugarcane Fields from Aerial Digital Images using Artificial Neural Network. Environmental Engineering and Management Journal, 20(5), 713-723. https://doi.org/10.30638/eemj.2021.068
  48. Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P., & Friborg, T. (2016). Estimating evaporation with thermal UAV data and two-source energy balance models. Hydrology and Earth System Sciences, 20(2), 697-713. https://doi.org/10.5194/hess-20-697-2016
  49. Huang, Y., Hoffmann, W. C., Lan, Y., Wu, W., & Fritz, B. K. (2009). Development of a spray system for an unmanned aerial vehicle platform. Applied Engineering in Agriculture, 25(6), 803-809. https://doi.org/10.13031/2013.29229
  50. Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed Technology, 32(1), 2-6. https://doi.org/10.1017/wet.2017.89
  51. Hunt, E. R., Cavigelli, M., Daughtry, C. S., Mcmurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6, 359-378. https://doi.org/10.1007/s11119-005-2324-5
  52. Inoue, S., Ito, A., & Yonezawa, C. (2020). Mapping Paddy fields in Japan by using a Sentinel-1 SAR time series supplemented by Sentinel-2 images on Google Earth Engine. Remote Sensing, 12(10), 1622. https://doi.org/10.3390/rs12101622
  53. Ishihara, M., Inoue, Y., Ono, K., Shimizu, M., & Matsuura, S. (2015). The impact of sunlight conditions on the consistency of vegetation indices in croplands—Effective usage of vegetation indices from continuous ground-based spectral measurements. Remote Sensing, 7(10), 14079-14098. https://doi.org/10.3390/rs71014079
  54. Islam, N., Rashid, M. M., Pasandideh, F., Ray, B., Moore, S., & Kadel, R. (2021). A review of applications and communication technologies for internet of things (IoT) and unmanned aerial vehicle (UAV) based sustainable smart farming. Sustainability, 13(4), 1821. https://doi.org/10.3390/su13041821
  55. Jia, L., Chen, X., Zhang, F., Buerkert, A., & Römheld, V. (2004). Use of digital camera to assess nitrogen status of winter wheat in the northern China plain. Journal of Plant Nutrition, 27(3), 441-450. https://doi.org/10.1081/PLN-120028872
  56. Joshi, P., Sandhu, K. S., Dhillon, G. S., Chen, J., & Bohara, K. (2024). Detection and monitoring of wheat diseases using unmanned aerial vehicles (UAVs). Computers and Electronics in Agriculture, 224, 109158. https://doi.org/10.1016/j.compag.2024.109158
  57. Kallimani, C., Heidarian, R., van Evert, F. K., Rijk, B., & Kooistra, L. (2020). UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture. Open Data Journal for Agricultural Research, 6, 1-7. https://doi.org/10.18174/odjar.v6i0.16317
  58. Kalaiselvi, P., Chaurasia, J., Krishnaveni, A., Krishnamoorthi, A., Singh, A., Kumar, V., ... & Labanya, R. (2024). Harvesting Efficiency: The Rise of Drone Technology in Modern Agriculture. Journal of Scientific Research and Reports, 30(6), 191-207. https://doi.org/10.9734/jsrr/2024/v30i62033
  59. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
  60. Kaniska, K., Jagadeeswaran, R., Kumaraperumal, R., Ragunath, K. P., Kannan, B., Muthumanickam, D., & Pazhanivelan, S. (2022). Impact of Drone Spraying of Nutrients on Growth and Yield of Maize Crop. International Journal of Environment and Climate Change, 12(11), 274-282. https://doi.org/10.9734/ijecc/2022/v12i1130972
  61. Kedari, S., Lohagaonkar, P., Nimbokar, M., Palve, G., & Yevale, P. (2016). Quadcopter-a smarter way of pesticide spraying. Imperial Journal of Interdisciplinary Research, 2(6), 1257-1260.
  62. Khanpara, B. M., Patel, B. P., Parmar, N. B., & Mehta, T. D. (2024). Transforming Agriculture with Drones: Applications, Challenges and Implementation Strategies. Journal of Scientific Research and Reports, 30(8), 792-802. https://doi.org/10.9734/jsrr/2024/v30i82299
  63. Khaspuria, G., Khandelwal, A., Agarwal, M., Bafna, M., Yadav, R., & Yadav, A. (2024). Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review. Journal of Scientific Research and Reports, 30(7), 671-686. https://doi.org/10.9734/jsrr/2024/v30i72180
  64. Kim, S. S., Kim, T. H., & Sim, J. S. (2019). Applicability assessment of UAV mapping for disaster damage investigation in Korea. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 209-214. https://doi.org/10.5194/isprs-archives-XLII-3-W8-209-2019
  65. Koondee, P., Saengprachathanarug, K., Posom, J., Watyotha, C., & Wongphati, M. (2019, August). Study of field capacity and variables of UAV operation time during spraying hormone fertilizer in sugarcane field. In IOP Conference Series: Earth and Environmental Science (Vol. 301, No. 1, p. 012020). IOP Publishing. https://doi.org/10.14456/apst.2023.93
  66. Lee, K., Sudduth, K. A., & Zhou, J. (2024). Evaluating UAV-Based Remote Sensing for Hay Yield Estimation. Sensors, 24(16), 5326. https://doi.org/10.3390/s24165326
  67. Leite‐Filho, A. T., de Sousa Pontes, V. Y., & Costa, M. H. (2019). Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. Journal of Geophysical Research: Atmospheres, 124(10), 5268-5281. https://doi.org/10.1029/2018JD029537
  68. Li, L., Fan, Y., Huang, X., & Tian, L. (2016). Real-time UAV weed scout for selective weed control by adaptive robust control and machine learning algorithm. 2016 ASABE Annual International Meeting. https://doi.org/10.13031/aim.20162462667
  69. Liebisch, F., Kirchgessner, N., Schneider, D., Walter, A., & Hund, A. (2015). Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods, 11, 1-20. https://doi.org/10.1186/s13007-015-0048-8
  70. Lou, Z., Xin, F., Han, X., Lan, Y., Duan, T., & Fu, W. (2018). Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy, 8(9), 187. https://doi.org/10.3390/agronomy8090187
  71. Lu, J., Dai, E., Miao, Y., & Kusnierek, K. (2024). Developing a new active canopy sensor-and machine learning-based in-season rice nitrogen status diagnosis and recommendation strategy. Field Crops Research, 317, 109540. https://doi.org/10.1016/j.fcr.2024.109540
  72. M. Tahat, M., M. Alananbeh, K., A. Othman, Y., & I. Leskovar, D. (2020). Soil health and sustainable agriculture. Sustainability, 12(12), 4859. https://doi.org/10.3390/su12124859
  73. Ma, Z., Zhu, X., Zhou, Z., Zou, X., & Zhao, X. (2019). A lateral-directional control method for high aspect ratio full-wing UAV and flight tests. Applied Sciences, 9(20), 4236. https://doi.org/10.3390/app9204236
  74. Malenovský, Z., Lucieer, A., King, D. H., Turnbull, J. D., & Robinson, S. A. (2017). Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods in Ecology and Evolution, 8(12), 1842-1857. https://doi.org/10.1111/2041-210X.12833
  75. Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., & Ciraolo, G. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sensing, 10(4), 641. https://doi.org/10.1111/2041-210X.12833
  76. Marinello, F., Pezzuolo, A., Chiumenti, A., & Sartori, L. (2016). Technical analysis of unmanned aerial vehicles (drones) for agricultural applications. Engineering for Rural Development, 15(2), 870-875. http://hdl.handle.net/11390/1099198
  77. Martos, V., Ahmad, A., Cartujo, P., & Ordoñez, J. (2021). Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Applied Sciences, 11(13), 5911. https://doi.org/10.3390/app11135911
  78. Memisoglu, O. (2019). Justification of Civilian Use of Drones and International Security: Comparison between the The United States and the European Union. https://doi.org/10.48676/unibo/amsdottorato/8899
  79. Merwe, D., Burchfield, D., Witt, T., Price, K., & Sharda, A. (2020). Chapter One—Drones in agriculture. Advances in Agronomy, 162, 1-30. https://doi.org/10.1016/bs.agron.2020.03.001
  80. Mogili, U. R., & Deepak, B. (2018). Review on the application of drone systems in precision agriculture. Procedia Computer Science, 133, 502-509. https://doi.org/10.1016/j.procs.2018.07.063
  81. Mohamed, M. B., Shukla, A. K., Keerthika, A., & Mehta, R. S. (2023). Pollination biology in henna evidences from semi-arid region-of Rajasthan. Indian Journal of Ecology, 50(3), 720-724.
  82. Mohsan, S. A. H., Zahra, Q. u. A., Khan, M. A., Alsharif, M. H., Elhaty, I. A., & Jahid, A. (2022). Role of drone technology helping in alleviating the COVID-19 pandemic. Micromachines, 13(10), 1593. https://doi.org/10.3390/mi13101593
  83. Monteiro, N. O. D. C., de Alencar, E. R., Souza, N. O. S., & Leão, T. P. (2021). Ozonized water in the preconditioning of corn seeds: physiological quality and field performance. Ozone: Science & Engineering, 43(5), 436-450. https://doi.org/10.1080/01919512.2020.1836472
  84. Mulero-Pázmány, M., Stolper, R., Van Essen, L., Negro, J. J., & Sassen, T. (2014). Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PloS one, 9(1), e83873. https://doi.org/10.1371/journal.pone.0083873
  85. Nandhini, D. U., Thiyagarajan, M., & Somasundaram, E. (2022). Soil fertility of rice-blackgram cropping sequence as influenced by different organic sources of nutrients. Bangladesh Journal of Botany, 51(2), 289-296. https://doi.org/10.3329/bjb.v51i2.60426
  86. Nauš, J., Prokopová, J., Řebíček, J., & Špundová, M. (2010). SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynthesis Research, 105, 265-271. https://doi.org/10.1007/s11120-010-9587-z
  87. Nhamo, L., Mabhaudhi, T., & Modi, A. (2019). Preparedness or repeated short-term relief aid? Building drought resilience through early warning in southern Africa. Water Sa, 45(1), 75-85. https://doi.org/10.4314/wsa.v45i1.09
  88. Nhamo, L., Magidi, J., Nyamugama, A., Clulow, A. D., Sibanda, M., Chimonyo, V. G., & Mabhaudhi, T. (2020). Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7), 256. https://doi.org/10.3390/agriculture10070256
  89. Ni, J., Yao, L., Zhang, J., Cao, W., Zhu, Y., & Tai, X. (2017). Development of an unmanned aerial vehicle-borne crop-growth monitoring system. Sensors, 17(3), 502. https://doi.org/10.3390/s17030502
  90. Niu, H., Zhao, T., Wang, D., & Chen, Y. (2019). Estimating evapotranspiration with UAVs in agriculture: A review. 2019 ASABE Annual International Meeting.:1901226. https://doi.org/10.13031/aim.201901226
  91. Olson, D., & Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 113(2), 971-992. https://doi.org/10.1002/agj2.20595
  92. Panjaitan, S. D., Dewi, Y. S. K., Hendri, M. I., Wicaksono, R. A., & Priyatman, H. (2022). A drone technology implementation approach to conventional paddy fields application. IEEE Access, 10, 120650-120658. https://doi.org/10.1109/ACCESS.2022.3221188
  93. Pathak, A. K., Sharma, M., & Nagar, P. K. (2020). A framework for PM2. 5 constituents-based (including PAHs) emission inventory and source toxicity for priority controls: A case study of Delhi, India. Chemosphere, 255, 126971. https://doi.org/10.1016/j.chemosphere.2020.126971
  94. Paul, P. L. C., Bell, R. W., Barrett-Lennard, E. G., Mainuddin, M., Maniruzzaman, M., & Sarker, K. K. (2022, August). Impact of Different Tillage Systems on the Dynamics of Soil Water and Salinity in the Cultivation of Maize in a Salt-Affected Clayey Soil of the Ganges Delta. In Transforming Coastal Zone for Sustainable Food and Income Security: Proceedings of the International Symposium of ISCAR on Coastal Agriculture, March 16–19, 2021 (pp. 101-116). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-95618-9_8
  95. Peña, J. M., Torres-Sánchez, J., de Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PloS one, 8(10), e77151. https://doi.org/10.1371/journal.pone.0077151
  96. Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: a systematic review of research. Journal of Sensors, 1, 1953085. https://doi.org/10.1155/2015/195308
  97. Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518. https://doi.org/10.1080/09720510.2017.1395171
  98. Qin, W. C., Qiu, B. J., Xue, X. Y., Chen, C., Xu, Z. F., & Zhou, Q. Q. (2016). Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Crop Protection, 85, 79-88. https://doi.org/10.1016/j.cropro.2016.03.018
  99. Rajabi, M. S., Beigi, P., & Aghakhani, S. (2023). Drone delivery systems and energy management: a review and future trends. Handbook of Smart Energy Systems, 1-19. https://doi.org/10.1007/978-3-030-72322-4_196-1
  100. Rajagopalan, R. P., & Krishna, R. (2018). Drones: Guidelines, regulations, and policy gaps in India. ICAO Scientific Review: Analytics and Management Research, 1, 53-68.
  101. Rathod, P. D., & Shinde, G. U. (2023). Autonomous Aerial System (UAV) for Sustainable Agriculture: A Review. International Journal of Environment and Climate Change, 13(8), 1343-1355. https://doi.org/10.9734/ijecc/2023/v13i82080
  102. Rejeb, A., Abdollahi, A., Rejeb, K., & Treiblmaier, H. (2022). Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture, 198, 107017. https://doi.org/10.1016/j.compag.2022.107017
  103. Ren, Q., Zhang, R., Cai, W., Sun, X., & Cao, L. (2020). Application and development of new drones in agriculture. IOP Conference Series Earth and Environmental Science 440(5):052041. https://doi.org/10.1088/1755-1315/440/5/052041
  104. Ribeiro, L. F. O., Vitória, E. L. d., Soprani Júnior, G. G., Chen, P., & Lan, Y. (2023). Impact of Operational Parameters on Droplet Distribution Using an Unmanned Aerial Vehicle in a Papaya Orchard. Agronomy, 13(4), 1138.1138. https://doi.org/10.3390/agronomy13041138
  105. Roldán, J. J., Joossen, G., Sanz, D., Cerro, J. D., & Barrientos, A. (2015). Mini-UAV based sensory system for measuring environmental variables in greenhouses. Sensors, 15(2), 3334-3350. https://doi.org/10.3390/s150203334
  106. Runde, D. F., Carter, P., Bandura, R., & Ramanujam, S. R. (2019). Innovations in Guarantees for Development. A Report of the CSIS Project on Prosperity and Development. 6-61
  107. Saeed, A. S., Younes, A. B., Cai, C., & Cai, G. (2018). A survey of hybrid unmanned aerial vehicles. Progress in Aerospace Sciences, 98, 91-105. https://doi.org/10.1016/j.paerosci.2018.03.007
  108. Sarghini, F., & De Vivo, A. (2017). Interference analysis of an heavy lift multirotor drone flow field and transported spraying system. Chemical Engineering Transactions, 58, 631-636. https://doi.org/10.3303/CET1758106
  109. Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., & Nansen, C. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, 17, 659-677. https://doi.org/10.1007/s11119-016-9442-0
  110. Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S., Zaidi, S. A. R., & Iqbal, N. (2019). Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors, 19, 3796. https://doi.org/10.3390/s19173796
  111. Simelli, I., & Tsagaris, A. (2015). The Use of Unmanned Aerial Systems (UAS) in Agriculture. HAICTA. Kavala, 730-736. http://ceur-ws.org/Vol-1498/HAICTA_2015_paper83.pdf
  112. Singh, P. (2023). Drones in Indian Agriculture: Trends, Challenges, and Policy Implications. https://doi.org/10.13140/RG.2.2.29651.35366/2
  113. Sinha, J. P. (2020). Aerial robot for smart farming and enhancing farmers' net benefit. The Indian Journal of Agricultural Sciences, 90(2), 258-267. https://doi.org/10.56093/ijas.v90i2.98997
  114. Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
  115. Stöcker, C., Bennett, R., Nex, F., Gerke, M., & Zevenbergen, J. (2017). Review of the current state of UAV regulations. Remote Sensing, 9(5), 459. https://doi.org/10.3390/rs9050459
  116. Subramanian, K., Pazhanivelan, S., Srinivasan, G., Santhi, R., & Sathiah, N. (2021). Drones in insect pest management. Frontiers in Agronomy, 3, 640885. https://doi.org/10.3389/fagro.2021.640885
  117. Swetha, M., Bharath Kumar, K., Sanwal Singh, M., & Urmila, M. (2024).Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discover Internet of Things, 4, 12. https://doi.org/10.1007/s43926-024-00066-5
  118. Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002
  119. Ukhurebor, K. E., Adetunji, C. O., Olugbemi, O. T., Nwankwo, W., Olayinka, A. S., Umezuruike, C., & Hefft, D. I. (2022). Precision agriculture: Weather forecasting for future farming. In AI, Edge and IoT-based Smart Agriculture (pp. 101-121). Elsevier. https://doi.org/10.4018/979-8-3693-1471-5.ch008
  120. Velusamy, P., Rajendran, S., Mahendran, R. K., Naseer, S., Shafiq, M., & Choi, J. G. (2022). Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges. Energies, 15(1), 217. https://doi.org/10.3390/en15010217
  121. Vairavan, C., Kamble, B. M., Durgude, A. G., Ingle, S. R., & Pugazenthi, K. (2024). Hyperspectral Imaging of Soil and Crop: A Review. Journal of Experimental Agriculture International, 46(1), 48-61. https://doi.org/10.9734/jeai/2024/v46i12290
  122. Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
  123. Wikifactory. (2020). A virtual round table on how UAVs and robotics can be used to make a difference. In Drones for Change. Retrieved from https://wikifactory.com/@niko11/stories/drones-for-change-a-round-table-on-how-uavs-and-robotics-can-be-used-to-make-a-difference
  124. Xia, T., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Gao, F., McKee, L., Prueger, J. H., Geli, H. M., Neale, C. M., & Sanchez, L. (2016). Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrology and Earth System Sciences, 20(4), 1523-1545. https://doi.org/10.5194/hess-20-1523-2016
  125. Yakushev, V., & Kanash, E. (2016). Evaluation of wheat nitrogen status by colorimetric characteristics of crop canopy presented in digital images. Journal of Agricultural Informatics, 7(1), 268. https://doi.org/10.17700/jai.2016.7.1.268
  126. Yang, C., Everitt, J. H., Bradford, J. M., & Murden, D. (2009). Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield. Transactions of the ASABE (American Society of Agricultural and Biological Engineers, 52, 641-649. https://doi.org/10.13031/2013.26816
  127. Yao, L., Jiang, Y., Zhiyao, Z., Shuaishuai, Y., & Quan, Q. (2016). A pesticide spraying mission assignment performed by multi-quadcopters and its simulation platform establishment. 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). https://doi.org/10.1109/CGNCC.2016.7829093
  128. Zhang, P., Zhang, W., Sun, H. T., He, F. G., Fu, H. B., Qi, L. Q., Yu, L. J., Jin, L. Y., Zhang, B., & Liu, J. S. (2021). Effects of Spray Parameters on the Effective Spray Width of Single-Rotor Drone in Sugarcane Plant Protection. Sugar Tech, 23, 308-315. https://doi.org/10.1007/s12355-020-00890-3
  129. Zhang, X. Q., Song, X. P., Liang, Y. J., Qin, Z. Q., Zhang, B. Q., Wei, J. J., Li, Y. R., & Wu, J. M. (2020). Effects of spray parameters of drone on the droplet deposition in sugarcane canopy. Sugar Tech, 22, 583-588. https://doi.org/10.1007/s12355-019-00792-z
  130. Zhao, R., Tang, W., Liu, M., Wang, N., Sun, H., Li, M., & Ma, Y. (2024). Spatial-spectral feature extraction for in-field chlorophyll content estimation using hyperspectral imaging. Biosystems Engineering, 246, 263-276. https://doi.org/10.1016/j.biosystemseng.2024.08.008
  131. Ziya, A., Mehmet, M., & Yusuf, Y. (2018). Determination of Sugar Beet Leaf Spot Disease Level (Cercospora beticola) with Image Processing Technique by Using Drone. Current Investigations in Agriculture and Current Research, 5(3), 621-631. https://doi.org/10.32474/CIACR.2018.05.000214
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