|
Adrian A M, Norwood S H, Mask P L. 2005. Producers’ perceptions and attitudes toward precision agriculture technologies. Computers and Electronics in Agriculture, 48, 256–271.
Avtar R, Watanabe T. 2020. Unmanned Aerial Vehicle: Applications in Agriculture and Environment. Springer, Singapore.
Bauer R A. 1960. Consumer behavior as risk taking. In: Cox D F, ed., Risk Taking and Information Handling in Consumer Behavior. Harvard University Press, Cambridge, Mass. pp. 389–398.
Beriya A. 2022. Application of drones in Indian agriculture. [2025-3-2]. https://csd.columbia.edu/sites/default/files/content/docs/ICT%20India/Papers/Final_Ag_Drones.pdf
Blasch G, Anberbir T, Negash T, Tilahun L, Belayineh F Y, Alemayehu Y, Mamo G, Hodson D P, Rodrigues Jr F A. 2023. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Scientific Reports, 13, 16768.
Chung J, Scherer M. 2019. China’s Agriculture Drone Revolution-Disruption in the Agriculture Ecosystem. Ipsos Business Consulting, Center of Excellence for Commercial Drone Adoption. Hong Kong.
Davis F D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Degieter M, De Steur H, Tran D, Gellynck X, Schouteten J J. 2023. Farmers’ acceptance of robotics and unmanned aerial vehicles: A systematic review. Agronomy Journal, 115, 2159–2173.
Featherman M. 2001. Extending the technology acceptance model by inclusion of perceived risk. In: Proceedings of the Americas Conference on Information Systems (AMCIS 2001). Association for Information Systems, Boston, MA. p. 148.
Featherman M S, Pavlou P A. 2003. Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human (Computer Studies), 59, 451–474.
Flett R, Alpass F, Humphries S, Massey C, Morriss S, Long N. 2004. The technology acceptance model and use of technology in New Zealand dairy farming. Agricultural Systems, 80, 199–211.
Gao P, Qi W, Liu S H, Liu Z, Pan Z H. 2023. Moving to a healthier city? An analysis of China’s internal population migration. Frontiers in Public Health, 11, 1132908.
Garbach K, Morgan G P. 2017. Grower networks support adoption of innovations in pollination management: The roles of social learning, technical learning, and personal experience. Journal of Environmental Management, 204, 39–49.
GOA (Government Accountability Office). 2024. Technology Assessment: Precision Agriculture Benefits and Challenges for Technology Adoption and Use. United States Government Accountability Office Report to Congressional Committees. Washington, D.C.
Goel R K, Yadav C S, Vishnoi S, Rastogi R. 2021. Smart agriculture - Urgent need of the day in developing countries. Sustainable Computing (Informatics and Systems), 30, 100512.
Han X, Lei Y T, Zhen T J, Huang Y. 2022. Analysis of factors influencing farmers’ willingness to continue using plant-protection UAV based on TAM. Journal of Southwest Minzu University (Natural Science Edition), 48, 332–339. (in Chinese)
Heitkämper K, Reissig L, Bravin E, Glück S, Mann S. 2023. Digital technology adoption for plant-protection: Assembling the environmental, labour, economic and social pieces of the puzzle. Smart Agricultural Technology, 4, 100148.
Hong K K, Kim Y G. 2002. The critical success factors for ERP implementation: An organizational fit perspective. Information and Management, 40, 25–40.
Hu P, Zhang R, Yang J, Chen L. 2022. Development status and key technologies of plant-protection UAVs in China: A review. Drones, 6, 354.
Jayashankar P, Nilakanta S, Johnston W J, Gill P, Burres R. 2018. IoT adoption in agriculture: The role of trust, perceived value and risk. Journal of Business & Industrial Marketing, 33, 804–821.
JFPDSDPMO (Jiangxi Farm Produce Distribution System Development Project Management Office). 2023. Jiangxi Farm Produce Distribution System Development Project: Pest Management Plan. [2025-1-11]. http://documents.worldbank.org/curated/en/099051423225519390
Kamal S A, Shafiq M, Kakria P. 2020. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Katz M L, Shapiro C. 1985. Network externalities, competition, and compatibility. The American Economic Review, 75, 424–440.
Kendall H, Clark B, Li W, Jin S, Jones G D, Chen J, Taylor, J, Li, Z, Frewer, L J. 2022. Precision agriculture technology adoption: A qualitative study of small-scale commercial “family farms” located in the North China Plain. Precision Agriculture, 23, 1–33.
Kim S, Garrison G. 2009. Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11, 323–333.
King A. 2017. Technology: The future of agriculture. Nature, 544, S21–S23.
Koundouri P, Nauges C, Tzouvelekas V. 2006. Technology adoption under production uncertainty: Theory and application to irrigation technology. American Journal of Agricultural Economics, 88, 657–670.
Kulbacki M, Segen J, Knieć W, Klempous R, Kluwak K, Nikodem J, Kulbacka J, Serester A. 2018. Survey of drones for agriculture automation from planting to harvest. In: 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES). IEEE (Institute of Electrical and Electronics Engineers), Las Palmas de Gran Canaria, Spain, 2018. pp. 353–358. doi: 10.1109/INES.2018.8523943.
Li M, Wang J, Zhao P, Chen K, Wu L. 2020. Factors affecting the willingness of agricultural green production from the perspective of farmers’ perceptions. Science of the Total Environment, 738, 140289.
Li K, Gharehgozli A, Lee J Y. 2023. Smart technologies and port operations: Optimal adoption strategy with network externality consideration. Computers & Industrial Engineering, 184, 109557.
Li J, Liu G, Chen Y, Li R. 2023. Study on the influence mechanism of adoption of smart agriculture technology behavior. Scientific Reports, 13, 8554.
Liu M, Liu H. 2024. Farmers’ adoption of agriculture green production technologies: Perceived value or policy-driven? Heliyon, 10, e23925.
Ma W, Renwick A, Grafton Q. 2018. Farm machinery use, off‐farm employment and farm performance in China. Australian Journal of Agricultural and Resource Economics, 62, 279–298.
Maddikunta P K R, Hakak S, Alazab M, Bhattacharya S, Gadekallu T R, Khan W Z, Pham Q V. 2021. Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors Journal, 21, 17608–17619.
Maertens A, Michelson H, Nourani V. 2020. How do farmers learn from extension services? Evidence from Malawi. American Journal of Agricultural Economics, 103, 569–595.
Marra M, Pannell D J, Ghadim A A. 2003. The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: Where are we on the learning curve? Agricultural Systems, 75, 215–234.
Michels M, Bonke V, Musshoff O. 2020. Understanding the adoption of smartphone apps in crop protection. Precision Agriculture, 21, 1209–1226.
Mohr S, Kühl R. 2021. Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22, 1816–1844.
Nazarov D, Nazarov A, Kulikova E. 2023. Drones in agriculture: Analysis of different countries. In: BIO Web of Conferences. EDP Sciences, Les Ulis, France.
Papadopoulos G, Arduini S, Uyar H, Psiroukis V, Kasimati A, Fountas S. 2024. Economic and environmental benefits of digital agricultural technologies in crop production: A review. Smart Agricultural Technology, 8, 100441.
Parmaksiz O, Cinar G. 2023. Technology acceptance among farmers: Examples of agricultural unmanned aerial vehicles. Agronomy, 13, 2077.
Pathak H S, Brown P, Best T. 2019. A systematic literature review of the factors affecting the precision agriculture adoption process. Precision Agriculture, 20, 1292–1316.
Quan X, Guo Q, Ma J, Doluschitz R. 2023. The economic effects of unmanned aerial vehicles in pesticide application: Evidence from Chinese grain farmers. Precision Agriculture, 24, 1965–1981.
Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I. 2020. A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148.
Rejeb A, Abdollahi A, Rejeb K, Treiblmaier H. 2022. Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture, 198, 107017.
Rogers E. 2003. The Diffusion of Innovations. 5th ed. The Free Press, New York.
Scherer R, Siddiq F, Tondeur J. 2019. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35.
SCIO (State Council Information Office of China). 2024. Drones soar into wider application in China. [2025-1-20]. http://english.scio.gov.cn/chinavoices/202407/19/content_117319224.htm
SCPRC (State Council of the People’s Republic of China). 2014. Several opinions on comprehensively deepening rural reform and accelerating the promotion of agricultural modernization. [2025-1-21]. https://www.gov.cn/gongbao/content/2014/content_2574736.htm (in Chinese)
Tucker C. 2017. Network stability, network externalities, and technology adoption. In: Entrepreneurship, Innovation, and Platforms (Advances in Strategic Management, Vol. 37), Emerald Publishing Limited, Leeds. pp. 151–175.
Urbahs A, Jonaite I. 2013. Features of the use of unmanned aerial vehicles for agriculture applications. Aviation, 17, 170–175.
Venkatesh V, Bala H. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39, 273–315.
Wachenheim C, Fan L, Zheng S. 2021. Adoption of unmanned aerial vehicles for pesticide application: Role of social network, resource endowment, and perceptions. Technology in Society, 64, 101470.
Wu Y, Xi X, Tang X, Luo D, Gu B, Lam S K, Vitousek, P M, Chen, D. 2018. Policy distortions, farm size, and the overuse of agricultural chemicals in China. Proceedings of the National Academy of Sciences of the United States of America, 115, 7010–7015.
Yigezu Y A, Mugera A, El-Shater T, Aw-Hassan A, Piggin C, Haddad A, Khalil, Y, Loss, S P. 2018. Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technological Forecasting and Social Change, 134, 199–206.
Yinka-Banjo C, Ajayi O. 2019. Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture. Unmanned Aerial Vehiclespublisher: IntechOpen, London, United Kingdom. pp. 107–128.
Zarei R, Zamani G H, Karimi H, Michaels E T. 2022. An extension of the technology acceptance model: Understanding farmers’ behavioral intention towards using agricultural E-commerce. International Journal of Agricultural Management and Development, 12, 27–42.
Zhang J, Wang J, Zhou X. 2019. Farm machine use and pesticide expenditure in maize production: Health and environment implications. International Journal of Environmental Research and Public Health, 16, 1808.
Zheng H, Zhou X, He J, Yao X, Cheng T, Zhu Y, Cao W, Tian Y. 2020. Early season detection of rice plants using RGB, NIR-GB and multispectral images from unmanned aerial vehicle (UAV). Computers and Electronics in Agriculture, 169, 105223.
Zheng S, Wang Z, Wachenheim C J. 2019. Technology adoption among farmers in Jilin Province, China: The case of aerial pesticide application. China Agricultural Economic Review, 11, 206–216.
Zhao Y. 2024. Jiangxi province promotes rural vitalization. China Daily. [2025-1-21]. https://www.chinadaily.com.cn/a/202403/07/WS65e98171a31082fc043bb427.html
|