人工智能在可持续农业水资源管理中的应用:综述

Ashoka, P, B. R. Devi, Nilesh Sharma, Madhumita Behera, Abhishek Gautam, Ayushi Jha, Gayatri Sinha
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引用次数: 0

摘要

人工智能(AI)能够加强可持续农业的水资源管理。在水资源有限和气候变化的背景下,对农业生产力和可持续发展的需求日益增长,这促使人们必须采取更有效的水资源管理措施。人工智能技术通过自动化精准灌溉系统、基于人工智能的预测模型和人工智能驱动的水质监测,显著提高了用水效率和农业产出。这些系统根据实时数据优化灌溉安排,提高施水精度,确保水质,从而支持可持续农业实践。然而,在水资源管理中应用人工智能并非没有挑战。使人工智能适应各种农业环境的技术困难、数据隐私和安全问题、伦理考虑以及小规模农户采用人工智能的障碍,都是需要解决的关键问题。本研究既探讨了整合人工智能技术的变革性影响,也探讨了其固有的挑战。此外,综述还指出了在人工智能对多变气候的适应性及其与社会经济数据的整合方面存在的研究空白,并建议未来的研究重点关注这些领域。报告还讨论了政策建议,强调需要制定标准和最佳实践,增加人工智能研究的资金和激励措施,促进培训和能力建设,并建立健全的数据管理监管框架。通过应对这些挑战并充分发挥人工智能的潜力,农业用水管理可以得到显著改善,从而提高全球用水安全和农业实践的可持续性。综述得出的结论是,虽然人工智能为农业用水管理带来了充满希望的未来,但要克服障碍并充分实现这项技术的效益,还需要采取战略性的周到方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in Water Management for Sustainable Farming: A Review
Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.
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