Integrating nutrient demand models and AI-based sensors with precision-dosing rigs to improve resource use and productivity, and reduce waste and emissions in commercial raspberry production.

将营养需求模型和基于人工智能的传感器与精密剂量装置相结合，以提高资源利用率和生产力，并减少商业覆盆子生产中的浪费和排放。

• 批准号: 51135
• 金额: $ 24.63万
• 依托单位: NETAFIM UK LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=51135
• 关键词: Integrating; nutrient; demand; models; AI

Soft fruit is an exciting product area with excellent growth potential. Although UK soft production is growing by ca. 8%/year, **demand for** berries by UK consumers still exceeds supply. Continued growth is needed to displace often inferior imports, but this must be achieved on a sustainable basis through efficient utilisation of valuable resources (primarily water and inorganic fertilisers) and minimal environmental impact.Soft fruit growers know that a sub-optimal supply of macro- and micro-nutrients will limit marketable yields and berry quality, but most guidelines on fertiliser inputs are hopelessly outdated. These formulations are often adjusted based on anecdotal observations by growers and agronomists, but there is little scientific basis to these amendments and many unneeded macro- and micro-nutrients accumulate in the substrate. Growers then apply irrigation flushing events to remove these harmful so-called "ballast ions" which wastes water, can result in lowered berry firmness, flavour and shelf-life, and poses a risk to local groundwater quality.Excessive N inputs often result in elevated emissions of N2O as a result of denitrification, and N2O emissions account for ca.44% (global warming potential \[GWP\] basis) of the total agriculture-related GHG emissions. CO2 has a GWP value of 1 while N2O has a value of 298, making the latter a more potent GHG. Reducing N inputs in agriculture and horticulture by more closely matching demand with supply should help to reduce N2O emissions, but this is a risky strategy if guidelines and monitoring sensors are not available.Our nutrient demand modelling work in IUK 102124 showed that N input to substrate-grown raspberry could be reduced by 32% without affecting marketable yields and berry quality, and overall water and fertiliser demand was lowered by 20% due to a reduction in plant biomass (less luxuriant growth). In a follow-up project IUK 102640, we have developed a prototype AI-based nitrogen / phosphorous / potassium (NPK) real-time sensor that growers can use to determine NPK availabilities in coir to inform their fertigation decision making. Initial testing has revealed a number of technical issues with the prototype system which need to be resolved before this strategy can be developed further. Work will be carried out with the sensor manufacturer to address sensor limitations and the output of this work will be evaluated under commercial conditions in 2022. We will also determine an effective exploitation strategy for the sensors as a stand-alone hand-held system and evaluate the integration of the sensors with irrigation controllers and new technologies to improve/automate crop nutrient management.This project will demonstrate the potential to combine a new variety-specific N demand model with a prototype sensor system that estimates NPK coir availabilities in real time. This approach will then be integrated into the NetBeat(tm) platform and combined with other technologies to deliver an automated control system. The SmartNutrigation system will maintain coir NPK availabilities within a narrow optimum range during each developmental stage using outputs from nutrient demand models and real-time feedback from the NPK sensors thereby maximising sustainability. It will deliver improvements in crop quality and productivity and improve nutrient and water-use efficiency, thereby creating a more sustainable production system.

软果是一个令人兴奋的产品领域，具有极好的增长潜力。尽管英国的软浆果产量每年增长约8%，但英国消费者对浆果的**需求仍然超过了供应。要取代往往劣质的进口，需要持续的增长，但这必须在可持续的基础上通过有效利用有价值的资源(主要是水和无机肥料)和最小的环境影响来实现。软果种植者知道，宏观和微观养分的次优供应将限制适销对路的产量和浆果质量，但大多数关于化肥投入的指导方针已经过时得无可救药。这些配方经常根据种植者和农学家的轶事观察进行调整，但这些修改几乎没有科学依据，许多不必要的宏微营养物质积累在基质中。然后，种植者应用灌溉冲洗事件来去除这些有害的所谓压载离子，这些离子浪费水，会导致浆果硬度、风味和货架期的降低，并对当地地下水质量构成风险。过量的氮素投入往往会导致反硝化导致N2O排放增加，N2O排放约占与农业相关的温室气体排放的44%(全球变暖潜力[GWP])。二氧化碳的GWP值为1，而N2O的GWP值为298，使后者成为一种更有效的温室气体。通过更紧密地匹配需求和供应来减少农业和园艺中的N投入应该有助于减少N2O排放，但如果没有指导方针和监测传感器，这是一个危险的策略。我们在IUK 102124中的营养需求建模工作表明，在不影响适销对路的产量和浆果质量的情况下，基质种植树莓的N投入可以减少32%，由于植物生物量的减少(生长不那么茂盛)，总的水和肥料需求减少了20%。在IUK 102640的后续项目中，我们开发了一种基于人工智能的氮磷钾实时传感器原型，种植者可以使用该传感器来确定椰子中氮磷钾的有效性，为他们的施肥决策提供信息。初步测试揭示了原型系统的一些技术问题，在进一步开发这一战略之前，需要解决这些问题。将与传感器制造商一起开展工作，以解决传感器的局限性，并将在2022年在商业条件下对这项工作的输出进行评估。我们还将确定传感器作为独立手持系统的有效开发策略，并评估传感器与灌溉控制器和改善/自动化作物养分管理的新技术的集成。该项目将展示将新的特定品种氮素需求模型与实时估计NPK Coir有效性的原型传感器系统相结合的潜力。然后，这种方法将被集成到NetBeat(Tm)平台，并与其他技术相结合，以提供一个自动化控制系统。SmartNutrigation系统将利用养分需求模型的输出和NPK传感器的实时反馈，在每个发育阶段将Coir NPK的有效性保持在一个狭窄的最佳范围内，从而最大限度地实现可持续性。它将改善作物质量和生产力，提高养分和水的利用效率，从而创造一个更可持续的生产系统。