INDIA: Reducing operational cost of vertical farms using online crop monitoring
INDIA: Reducing operational cost of vertical farms using online crop monitoring
批准号:
10025354
负责人:
金额:
$1.27万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --
中文摘要
该项目为实现垂直耕作(VF)技术提供了突破性进展。与传统农业相比,这些系统减少了用水量,消除了农用化学品的使用,并提供了全年的本地生产。我们希望更多地采用可持续的粮食种植系统,以减少作物生产对环境的影响,并为英国农业技术的增长和出口创造机会。该项目的重点是降低VF系统的运营成本,使英国的技术和种植技术财富能够在优化的种植系统中实现。主要目标是:将收益率提高30%;并将运营成本降低25%。它将把领先的创新集成到一个可扩展的、商业上可行的、交钥匙解决方案演示中。该系统将降低建造、控制和监测最佳生长条件的复杂性和成本。它将为种植者提供更好的信息和正确的数据,以做出决策和对检测到的变化做出自动化反应,从而实现更高质量、更高产量的产品,同时更好地装备他们以适应市场需求并降低业务失败的风险。FOTENIX将其主动多光谱视觉功能整合到VF系统中,实现了对作物健康和生长状态的实时数据反馈。这项技术允许开发一个集成的决策支持系统,用于自动控制照明、营养和气候。
英文摘要
This project provides step-change advances towards enabling Vertical farming (VF) technologies. In comparison to traditional agriculture, these systems reduce water usage, eliminate the use of agrochemicals and provide year-round, local production. We look to increase the adoption of sustainable food growing systems, which reduces the environmental impact of crop production and create opportunities for the growth and export of UK agricultural technologies.The focus of the project is to reduce the operational costs of VF systems and enable the wealth of UK technical and growing know-how in an optimised growing system. The key objectives are: improve yield by 30%; and reduce operational costs by 25%. It will integrate leading innovations into a scalable, commercially viable, turn-key solution demonstrator. This system will reduce the complexity and costs of constructing, controlling and monitoring optimal growth conditions. It will provide growers with better information and the right data to make decisions and automate responses to changes detected, enabling higher quality, higher yield produce, while better equipping them to adapt to market demand and reducing the risks of business failures.FOTENIX incorporate their active multispectral vision capabilities into the VF system, enabling real-time data feedback on crop health and growth status. This technology allows the development of an integrated decision-support system for the automated control of the lighting, nutrients and climate.
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