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

ماشین های کشاورزی

مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 01 تیر 1404   

نوع مقاله: مقاله مروری لاتین

شناسه دیجیتال (DOI): 10.22067/jam.2024.89334.1276

نویسندگان

اس. ریشیکساوان¹؛ پی. کنان^{* 2}؛ اس. پاژانیولان²؛ آر. کومارپرومال¹؛ ان. شریثارن³؛ دی. موهومانیکام¹؛ ام. محمد روشن ابو فرناس⁴؛ بی. ونکاتش⁵

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

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

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

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

⁵گروه 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

بهینه‌سازی منابع؛ تصمیم‌گیری مبتنی بر داده؛ کشاورزی دقیق؛ نظارت بر محصول؛ وسایل نقلیه هوایی بدون سرنشین (پهپاد)

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 fif^th 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