[1] Abrougui, K., Guebsi, R., Ouni, A., Boughattas, N. E., Habel, F., Barkaoui, Y., Amami, R., Khemis, C., & Kefauver, S. (2022). Contribution of UAV-airborne imagery in the study of machine-soil-plant interaction in potato cultivation, 4(2), 71-78. 10.56027/JOASD.spiss102022|
[2] Ali, M. A., Dhanaraj, R. K., & Kadry, S. (2024). AIenabled IoT-based pest prevention and controlling system using sound analytics in large agricultural field. Computers and Electronics in Agriculture, 220, 108844. https://doi.org/10.1016/j.compag.2024.108844
[3] Altan, A., & Hacıoğlu, R. (2020). Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances. Mechanical Systems and Signal Processing, 138, 106548. https://doi.org/10.1016/j.ymssp.2019.106548
[4] Awais, M., Naqvi, S. M. Z. A., Zhang, H., Li, L., Zhang, W., Awwad, F. A., Ismail, E. A. A., Khan, M. I., Vijaya, R., & Hu, J. (2023). AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresources and Bioprocessing, 10(1), 90. https://doi.org/10.1186/s40643-023-00710-y
[5] Balaji, K., Babu, V., & Sulthan, S. (2022). Design and development of multipurpose agriculture drone using lightweight materials. SAE International Journal of Aerospace, 16(01-16-02-0012), 177-183. https://doi.org/10.4271/01-16-02-0012
[6] Beniwal, G., & Singhrova, A. (2022). A systematic literature review on IoT gateways. Journal of King Saud University-Computer and Information Sciences, 34(10), 9541-9563. https://doi.org/10.1016/j.jksuci.2021.11.007
[7] Borikar, G. P., Gharat, C., & Deshmukh, S. R. (2022, October). Application of drone systems for spraying pesticides in advanced agriculture: A Review. In IOP Conference Series: Materials Science and Engineering (Vol. 1259, No. 1, p. 012015). IOP Publishing. 10.1088/1757-899X/1259/1/012015
[8] Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. https://doi.org/10.1016/j.iot.2020.100187
[9] Canicattì, M., & Vallone, M. (2024). Drones in vegetable crops: a systematic literature review. Smart Agricultural Technology, 7(1), 100396. https://doi.org/10.1016/j.atech.2024.100396
[10] Channe, H., Kothari, S., & Kadam, D. (2015). Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis. Int. J. Computer Technology & Applications, 6(3), 374-382. https://www.researchgate.net/publication/323187556
[11] Chen, G., Du, W., Xu, T., Wang, S., Qi, X., & Wang, Y. (2024). Investigating Enhanced YOLOv8 Model Applications for Large-Scale Security Risk Management and Drone-Based Low-Altitude Law Enforcement. Highlights in Science, Engineering and Technology, 98(1), 390-396.
[12] Chen, H., Lan, Y., Fritz, B. K., Hoffmann, W. C., & Liu, S. (2021). Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Agricultural and Biological Engineering, 14(1), 38-49. https://dx.doi.org/10.25165/j.ijabe.20211401.5714
[13] Chen, R., Meng, Q., & Yu, J. J. (2023). Optimal government incentives to improve the new technology adoption: Subsidizing infrastructure investment or usage?. Omega, 114(1), 102740.https://doi.org/10.1016/j.omega.2022.102740
[14] Crusiol, L. G. T., Sun, L., Sun, Z., Chen, R., Wu, Y., Ma, J., & Song, C. (2022). In-season monitoring of maize leaf water content using ground-based and UAV-based hyperspectral data. Sustainability, 14(15), 9039. https://doi.org/10.3390/su14159039
[15] Dai, K., Shen, S., & Cheng, C. (2022). Evaluation and analysis of the projected population of China. Scientific Reports, 12(1), 3644. https://doi.org/10.1038/s41598-022-07646-x
[16] Faiçal, B. S., Freitas, H., Gomes, P. H., Mano, L. Y., Pessin, G., de Carvalho, A. C., Krishnamachari, B., & Ueyama, J. (2017). An adaptive approach for UAV-based pesticide spraying in dynamic environments. Computers and Electronics in Agriculture, 138(1), 210-223. https://doi.org/10.1016/j.compag.2017.04.011
[17] Farhan, S. M., Yin, J., Chen, Z., & Memon, M. S. (2024). A comprehensive review of LiDAR applications in crop management for precision agriculture. Sensors (Basel, Switzerland), 24(16), 5409. https://doi.org/10.3390/s24165409
[18] Frauendorf, J. L., & Almeida de Souza, É. (2022). The different architectures used in 1G, 2G, 3G, 4G, and 5G networks. In The Architectural and Technological Revolution of 5G (pp. 83-107). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-10650-7_7
[19] Fue, K. G., Porter, W. M., Barnes, E. M., & Rains, G. C. (2020). An extensive review of mobile agricultural robotics for field operations: focus on cotton harvesting. AgriEngineering, 2(1), 150-174. https://doi.org/10.3390/agriengineering2010010
[20] Haseeb, K., Ud Din, I., Almogren, A., & Islam, N. (2020). An energy efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20(7), 2081. https://doi.org/10.3390/s20072081
[21] He, G., Li, C., Song, M., Shu, Y., Lu, C., & Luo, Y. (2023). A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment. Ad Hoc Networks, 149(1), 103249. https://doi.org/10.1016/j.adhoc.2023.103249
[22] Hiraguri, T., Shimizu, H., Kimura, T., Matsuda, T., Maruta, K., Takemura, Y., ... & Takanashi, T. (2023). Autonomous drone-based pollination system using AI classifier to replace bees for greenhouse tomato cultivation. IEEE Access.10.1109/ACCESS.2023.3312151
[23] Hongbo, C., Hansen, E. H., & Růǐčka, J. (1985). Evaluation of critical parameters for measurement of pH by flow injection analysis determination of pH in soil extracts. Analytica chimica acta, 169, 209-220. https://doi.org/10.1016/S0003-2670(00)86223-6
[24] Hossain, A., Krupnik, T. J., Timsina, J., Mahboob, M. G., Chaki, A. K., Farooq, M., Bhatt, R., Fahad, S., & Hasanuzzaman, M. (2020). Agricultural land degradation: processes and problems undermining future food security. In Environment, climate, plant and vegetation growth (pp. 17-61). Cham: Springer International Publishing.
[25] Huang, Y., Hoffman, W. C., Lan, Y., Fritz, B. K., & Thomson, S. J. (2015). Development of a lowvolume sprayer for an unmanned helicopter. Journal of Agricultural Science, 7(1), 148. http://dx.doi.org/10.5539/jas.v7n1p148
[26] Hunt Jr, E. R., & Daughtry, C. S. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International Journal of Remote Sensing, 39(15-16), 5345-5376. https://doi.org/10.1080/01431161.2017.1410300
[27] Jain, S., Choudhari, P., & Srivastava, A. (2021). The fundamentals of Internet of Things: architectures, enabling technologies, and applications. In Healthcare Paradigms in the Internet of Things Ecosystem (pp. 1-20). Academic Press. https://doi.org/10.1016/B978-0-12-819664-9.00001-6
[28] Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001
[29] Jenssen, R. O. R., Eckerstorfer, M., & Jacobsen, S. (2019). Drone-mounted ultrawideband radar for retrieval of snowpack properties. IEEE Transactions on Instrumentation and Measurement, 69(1), 221-230. 10.1109/TIM.2019.2893043
[30] Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12. https://doi.org/10.1016/j.aiia.2019.05.004
[31] Jiang, Z., & Xu, C. (2023). Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China. Energy Policy, 177, 113527. https://doi.org/10.1016/j.enpol.2023.113527
[32] Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198(1), 105-114. https://doi.org/10.1016/j.rse.2017.06.007
[33] Kerkech, M., Hafiane, A., & Canals, R. (2020). Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Computers and Electronics in Agriculture, 174(1), 105446. https://doi.org/10.1016/j.compag.2020.105446
[34] Khadatkar, A., Mathur, S. M., Dubey, K., & Magar, A. P. (2021). Automatic Ejection of Plug-type Seedlings using Embedded System for use in Automatic Vegetable Transplanter. Journal of Sensors, 2021(1), 305-312.
[35] Khuzaimah, Z., Nawi, N. M., Adam, S. N., Kalantar, B., Emeka, O. J., & Ueda, N. (2022). Application and potential of drone technology in oil palm plantation: Potential and limitations. Journal of Sensors, 2022(1), 5385505. https://doi.org/10.1155/2022/5385505
[36] Laghari, A. A., Jumani, A. K., Laghari, R. A., & Nawaz, H. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, 3, 8-22. https://doi.org/10.1016/j.cogr.2022.12.004
[37] Lan, Y., Thomson, S. J., Huang, Y., Hoffmann, W. C., & Zhang, H. (2010). Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and electronics in agriculture, 74(1), 34-38. https://doi.org/10.1016/j.compag.2010.07.001
[38] Liu, J., Zhu, Y., Tao, X., Chen, X., & Li, X. (2022). Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery. Frontiers in Plant Science, 13, 1032170. https://doi.org/10.3389/fpls.2022.1032170
[39] Lu, B., Dao, P. D., Liu, J., He, Y., & Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12(16), 2659. https://doi.org/10.3390/rs12162659
[40] Mahroof, K., Omar, A., Rana, N. P., Sivarajah, U., & Weerakkody, V. (2021). Drone as a Service (DaaS) in promoting cleaner agricultural production and Circular Economy for ethical Sustainable Supply Chain development. Journal of Cleaner Production, 287,125522. https://doi.org/10.1016/j.jclepro.2020.125522
[41] Maimaitijiang, M., Sagan, V., Erkbol, H., Adrian, J., Newcomb, M., LeBauer, D., LeBauer, D., Pauli, D., Shakoor, N., & Mockler, T. C. (2020). UAV-based sorghum growth monitoring: A comparative analysis of lidar and photogrammetry. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 3, 489-496. https://doi.org/10.5194/isprsannals-V-3-2020-489-2020
[42] Makam, S., Komatineni, B. K., Meena, S. S., & Meena, U. (2024). Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discover Internet of Things, 4(1), 12. https://doi.org/10.1007/s43926-024-00066-5
[43] Mao, F., Khamis, K., Clark, J., Krause, S., Buytaert, W., Ochoa-Tocachi, B. F., & Hannah, D. M. (2020). Moving beyond the technology: a socio-technical roadmap for low-cost water sensor network applications. Environmental Science & Technology, 54(15), 9145-9158. https://doi.org/10.1021/acs.est.9b07125
[44] Marzuki, O. F., Teo, E. Y. L., & Rafie, A. S. M. (2021). The mechanism of drone seeding technology: a review. Malays. For, 84, 349-358.
[45] Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., Belli, C., Zaldei, A., Bianconi, R., & Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote sensing, 7(3), 2971-2990. https://doi.org/10.3390/rs70302971
[46] Mattivi, P., Pappalardo, S. E., Nikolić, N., Mandolesi, L., Persichetti, A., De Marchi, M., & Masin, R. (2021). Can commercial low-cost drones and open-source GIS technologies be suitable for semi-automatic weed mapping for smart farming? A case study in NE Italy. Remote sensing, 13(10), 1869. https://doi.org/10.3390/rs13101869
[47] Ming, R., Jiang, R., Luo, H., Lai, T., Guo, E., & Zhou, Z. (2023). Comparative analysis of different uav swarm control methods on unmanned farms. Agronomy, 13(10), 2499. https://doi.org/10.3390/agronomy13102499
[48] Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
[49] Narayana, T. L., Venkatesh, C., Kiran, A., Kumar, A., Khan, S. B., Almusharraf, A., & Quasim, M. T. (2024). Advances in real time smart monitoring of environmental parameters using IoT and sensors. Heliyon, 10(7), 28195. https://doi.org/10.1016/j.heliyon.2024.e28195
[50] Nazarov, D., Nazarov, A., & Kulikova, E. (2023). Drones in agriculture: Analysis of different countries. In BIO Web of Conferences (Vol. 67, p. 02029). EDP Sciences. https://doi.org/10.1051/bioconf/20236702029
[51] Nex, F., Armenakis, C., Cramer, M., Cucci, D. A., Gerke, M., Honkavaara, E., Kukko, A., Persello, C., Skaloud, J., & Skaloud, J. (2022). UAV in the advent of the twenties: Where we stand and what is next. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 215-242. https://doi.org/10.1016/j.isprsjprs.2021.12.006
[52] Oliveira, R. C. D., & Silva, R. D. D. S. E. (2023). Artificial intelligence in agriculture: benefits, challenges, and trends. Applied Sciences, 13(13), 7405. https://doi.org/10.3390/app13137405
[53] Pádua, L., Adão, T., Sousa, A., Peres, E., & Sousa, J. J. (2020). Individual grapevine analysis in a multi-temporal context using UAV-based multi-sensor imagery. Remote Sensing, 12(1), 139. https://doi.org/10.3390/rs12010139
[54] Pansy, D. L., & Murali, M. (2023). UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN. Environmental Monitoring and Assessment, 195(9), 1120.
[55] Prakash, C., Singh, L. P., Gupta, A., & Lohan, S. K. (2023). Advancements in smart farming: A comprehensive review of IoT, wireless communication, sensors, and hardware for agricultural automation. Sensors and Actuators A: Physical, 362(1), 114605. https://doi.org/10.1016/j.sna.2023.114605
[56] Puppala, H., Peddinti, P. R., Tamvada, J. P., Ahuja, J., & Kim, B. (2023). Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India. Technology in Society, 74, 102335. https://doi.org/10.1016/j.techsoc.2023.102335
[57] Shamshiri, R., Weltzien, C., Hameed, I. A., J Yule, I., E Grift, T., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A Perspective of Digital Farming., 11(4), 1-14. http://dx.doi.org/10.25165/j.ijabe.20181104.4278
[58] Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148
[59] Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., & Chatterjee, S. (2019). Climate change has likely already affected global food production. PloS one, 14(5), e0217148. https://doi.org/10.1371/journal.pone.0217148
[60] Rinaldi, M., Primatesta, S., & Guglieri, G. (2023). A comparative study for control of quadrotor UAVs. Applied Sciences, 13(6), 3464. https://doi.org/10.3390/app13063464
[61] Schmidt, R., Schadow, J., Eißfeldt, H., & Pecena, Y. (2022). Insights on remote pilot competences and training needs of civil drone pilots. Transportation research procedia, 66, 1-7. https://doi.org/10.1016/j.trpro.2022.12.001
[62] Seong, M., Jo, O., & Shin, K. (2024). Age of information minimization in UAV-assisted data harvesting networks by multi-agent deep reinforcement curriculum learning. Expert Systems with Applications, 255(1), 124379. https://doi.org/10.1016/j.eswa.2024.124379
[63] Singh, N., Gupta, D., Joshi, M., Yadav, K., Nayak, S., Kumar, M., Nayak, K., Gulaiya, S., & Rajpoot, A. S. (2024). Application of Drones Technology in Agriculture: A Modern Approach. Journal of Scientific Research and Reports, 30(7), 142-152. https://doi.org/10.9734/jsrr/2024/v30i72131
[64] Song, Y., Bi, J., & Wang, X. (2024). Design and implementation of intelligent monitoring system for agricultural environment in IoT. Internet of Things, 25, 101029. https://doi.org/10.1016/j.iot.2023.101029
[65] Souvanhnakhoomman, S. (2024). Review on application of drone in spraying pesticides and fertilizers. arXiv preprint arXiv:2402.00020. https://doi.org/10.48550/arXiv.2402.00020
[66] Sundmaeker, H., Verdouw, C., Wolfert, S., & Freire, L. P. (2022). Internet of food and farm 2020. In Digitising the industry internet of things connecting the physical, digital and virtualworlds (pp. 129-151). River Publishers.
[67] Toscano, F., Fiorentino, C., Capece, N., Erra, U., Travascia, D., Scopa, A., Drosos, M., & D’Antonio, P. (2024). Unmanned Aerial Vehicle for Precision Agriculture: A Review. IEEE Access., 12(1), 69188-69205. https://doi.org/10.1109/ACCESS.2024.3401018
[68] Townsend, A., Jiya, I. N., Martinson, C., Bessarabov, D., & Gouws, R. (2020). A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements. Heliyon, 6(11), e05285. https://doi.org/10.1016/j.heliyon.2020.e05285
[69] Wei, X., Xie, B., Wan, C., Song, R., Zhong, W., Xin, S., & Song, K. (2024). Enhancing soil health and plant growth through microbial fertilizers: Mechanisms, benefits, and sustainable agricultural practices. Agronomy, 14(3), 609. https://doi.org/10.3390/agronomy14030609
[70] Yin, X., Jin, R., & Lin, D. (2024, June). Efficient airto-air drone detection with composite multi-dimensional attention. In 2024 IEEE 18th International Conference on Control & Automation (ICCA) (pp. 725-730). IEEE. https://doi.org/10.1109/ICCA62789.2024.10591905
[71] Zhou, Y., Lao, C., Yang, Y., Zhang, Z., Chen, H., Chen, Y., Chen, J., Ning, J., Yang, N., & Yang, N. (2021). Diagnosis of winter-wheat water stress based on UAVborne multispectral image texture and vegetation indices. Agricultural Water Management, 256, 107076. https:// doi.org/10.1016/j.agwat.2021.107076
