利用自动化和数据驱动物流来实现新兴经济体高价值作物的可持续农业
Naoum Tsolakis1, Tomás Seosamh Harrington2, Jagjit Singh Srai1
1Centre for International Manufacturing, Institute for Manufacturing (IfM), Department of Engineering, School of Technology, University of Cambridge, Cambridge CB3 0FS, United Kingdom.
Smart agricultural technology
|August 2, 2023
概括
这项研究介绍了物联网 (IoT) 和自动引导车辆 (AGV) 在金诺农作物中用于精确灌,优化了Punjab的资源使用和农民福祉. 该研究强调了可持续农业和实现可持续发展目标 (SDGs) 的数据驱动物流.
科学领域:
- 农业技术 农业技术
- 环境科学 环境科学
- 运营研究 运营研究
背景情况:
- 农业创新对于可持续发展目标 (SDGs) 至关重要,特别是在印度旁遮普邦等资源稀缺地区,不高效的做法导致过度投入使用.
- 现有的农田物流研究往往忽视了环境和能源影响,对物联网 (IoT) 和用于精确灌计划的自动引导车辆 (AGV) 的探索有限.
研究的目的:
- 调查使用物联网的基于数据的灌实践对自然资源使用,农业效率和小农农户福利在金诺种植中的影响.
- 解决了解农业先进灌技术对环境,能源和社会经济影响的差距.
主要方法:
- 进行了文献分类,以确定农业领域物流中关于环境和能源影响的研究缺口.
- 实证地研究了Kinnow种植的四种灌场景 (洪水,手动,AGV通知的手动,AGV辅助).
- 该AGV辅助灌场景被建模为容量化车辆路线问题,以分析对水能纽带的可持续性影响.
主要成果:
- 该研究比较了不同灌实践对水能关系的可持续性影响.
- 数据驱动的灌显著影响了资源配置,运营效率和农民福祉.
- 与传统方法相比,AGV辅助灌显示出优化资源利用的潜力.
结论:
- 数据驱动的技术创新,包括物联网和AGV,对于过渡到以SDG为中心的食品供应链至关重要.
- 实施精确的灌规划可以提高农业经营的环境和经济可持续性.
- 为支持可持续农业学,为社区主导的现场物流规划提供了指导原则.
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