A Study on the Pathways for Promoting Agricultural Modernization in the Hainan Free Trade Port Through New-Quality Productive Forces
Main Article Content
Abstract
The construction of the Hainan Free Trade Port has created an environment for agricultural modernization by opening up the system, unfettering the flow of factors and upgrading industry. To reveal the paths of how new-quality productive forces promote the modernization of agriculture in Hainan, this paper builds an evaluation index system on production efficiency, technological innovation, digital empowerment, green transformation, industrial integration, and open circulation. For measurement and analysis, an obstacle degree model and entropy weighting model which is comprehensive evaluation model were used. The results indicate that, during 2018–2024, the comprehensive agricultural modernization index of Hainan has increased from 0.392 to 0.588, where digital empowerment, technological innovation, and open circulation have contributed 53.0% to the index increase, and the sub-indices such as cold chain coverage, processing and conversion, commercialization of scientific and technological results, and quality traceability are the main limitations of the comprehensive agricultural modernization. The conclusions suggest that the institutional setting of the Free Trade Port should take advantage of the synergy of technology platforms, digital government, cold chain processing and brand certification to foster the transformation of Hainan's tropical specialty agriculture into high quality and modern agriculture.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
D. Radočaj, M. Jurišić, I. Plaščak, et al., “A Bibliometric Analysis of Machine and Deep Learning in Remote Sensing for Precision Agriculture,” Agronomy, vol. 16, no. 8, pp. 807–807, 2026. DOI: 10.3390/AGRONOMY16080807.
A. S. Gafaar, O. P. Boamah, J. Onumah, et al., “Integrating Machine Learning and Drone Technology for Precision Agriculture: A Smart Solution for Automated Irrigation and Crop Management,” Environmental Challenges, vol. 22, pp. 101393– 101393, 2026. DOI: 10.1016/J.ENVC.2025.101393.
A. Morchid, Z. Said, and H. Tairi, “Innovative applications of the Internet of Things and machine learning in sustainable agricultural irrigation management: Benefits and challenges,” Smart Agricultural Technology, vol. 13, pp. 101661–101661, 2026. DOI: 10.1016/J.ATECH.2025.101661.
D. Radoˇcaj, P. Radoˇcaj, I. Plašˇcak, et al., “Evolution of Deep Learning Approaches in UAV-Based Crop Leaf Disease Detection: A Web of Science Review,” Applied Sciences, vol. 15, no. 19, pp. 10778–10778, 2025. DOI: 10.3390/APP151910778.
M. ٞagiewska and P. Chwastyk, “E. Integrating Remote Sensing and Autonomous Robotics in Precision Agriculture: Current Applications and Workflow Challenges,” Agronomy, vol. 15, no. 10, pp. 2314–2314, 2025. DOI: 10.3390/AGRON-OMY15102314.
T. Miller, G. Mikiciuk, I. Durlik, et al., “The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies,” Sensors, vol. 25, no. 12, pp. 3583–3583, 2025. DOI: 10.3390/S25123583.
L. F. Macedo, H. Nóbrega, D. R. G. J. Freitas, et al., “Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards,” Remote Sensing, vol. 17, no. 11, pp. 1899–1899, 2025. DOI: 10.3390/RS17111899.
V. Anand, P. Rajput, T. Minkina, et al., “Systematic Review of Machine Learning Applications in Sustainable Agriculture: Insights on Soil Health and Crop Improvement,” Phyton—International Journal of Experimental Botany, vol. 94, no. 5, pp. 1339–1365, 2025. DOI: 10.32604/PHYTON.2025.063927.
Chatrabhuj, K. Meshram, U. Mishra, et al., “Application of Artificial Intelligence in Agri-Tech, Environmental, and Biodiversity Conservation,” Array, vol. 26, pp. 100412–100412, 2025. DOI: 10.1016/J.ARRAY.2025.100412.
R. Ahsen, D. P. Bitonto, P. Novielli, et al., “Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review,” Applied Sciences, vol. 15, no. 8, pp. 4228–4228, 2025. DOI: 10.3390/APP15084228.
L. J. F. Pérez, H. D. R. Alonso, L. G. Leazaún, et al., “Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices,” Computers and Electronics in Agriculture, vol. 231, pp. 110018–110018, 2025. DOI: 10.1016/J.COMPAG.2025.110018.
A. Upadhyay, S. N. Chandel, P. K. Singh, et al., “Deep Learning and Computer Vision in Plant Disease Detection: A Comprehensive Review of Techniques, Models, and Trends in Precision Agriculture,” Artificial Intelligence Review, vol. 58, no. 3, pp. 92–92, 2025. DOI: 10.1007/S10462-024-11100-X.
R. Guebsi, S. Mami, and K. Chokmani, “Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges,” Drones, vol. 8, no. 11, pp. 686–686, 2024. DOI: 10.3390/DRONES8110686.
R. Íñiguez, S. Gutiérrez, P. C. Echeverría, et al., “Deep Learning Modeling for Non-Invasive Grape Bunch Detection under Diverse Occlusion Conditions,” Computers and Electronics in Agriculture, vol. 226, pp. 109421–109421, 2024. DOI: 10.1016/J.COMPAG.2024.109421.
G. P. Peterson, D. J. Shepherd, L. R. Hill, et al., “Remote Sensing Guides Management Strategy for Invasive Legumes on the Central Plateau, New Zealand,” Remote Sensing, vol. 16, no. 13, pp. 2503–2503, 2024. DOI: 10.3390/RS16132503.
V. Kumar, V. K. Sharma, N. Kedam, et al., “A comprehensive review on smart and sustainable agriculture using IoT technologies,” Smart Agricultural Technology, 8, 100487-, 2024. DOI: 10.1016/J.ATECH.2024.100487.
E. M. Jarroudi, L. Kouadio, P. Delfosse, et al., “Leveraging edge artificial intelligence for sustainable agriculture,” Nature Sustainability, 7 (7): 846–8 54, 2024. DOI: 10.1038/S41893-024-01352-4.
C. A. Tagarakis, L. Benos, G. Kyriakarakos, et al., “Digital Twins in Agriculture and Forestry: A Review,” Sensors, vol. 24, no. 10, 2024. DOI: 10.3390/S24103117.
A. Soussi, E. Zero, R. Sacile, et al., “Smart Sensors and Smart Data for Precision Agriculture: A Review,” Sensors (Basel, Switzerland), 24 (8): 2647–, 2024. DOI: 10.3390/S24082647.
S. Gokool, M. Mahomed, A. Clulow, et al., “Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review,” Drones, vol. 8, no. 3, 2024. DOI: 10.3390/DRONES8030081.
A. V. M. N. S., I. A, et al., “NIR-hyperspectral imaging and machine learning for non-invasive chemotype classification in Cannabis sativa L,” Computers and Electronics in Agriculture, 217, 108551–, 2024. DOI: 10.1016/J.COMPAG.2023.108551.
C. Alberto, S. Marco, and M. Francesco, “The Segment Anything Model (SAM) for accelerating the smart farming revolution,” Smart Agricultural Technology, vol. 6, 2023. DOI: 10.1016/J.ATECH.2023.100367.
M. B. M. E. Karunathilake, T. A. Le, S. Heo, et al., “The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture,” Agriculture, vol. 13, no. 8, 2023. DOI: 10.3390/AGRICULTURE13081593.