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.

软果是一个令人兴奋的产品领域，具有良好的增长潜力。尽管英国的软生产正在增长。8%/年，英国消费者对 ** 浆果的需求仍然超过供应。持续的增长需要取代劣质的进口，但这必须在可持续的基础上通过有效利用宝贵的资源（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，次优的宏量和微量营养素供应将限制可销售的产量和浆果质量，但大多数关于肥料投入的指导方针都已经过时。这些配方通常根据种植者和农学家的轶事观察进行调整，但这些修正几乎没有科学依据，许多不需要的大量和微量营养素积累在基质中。然后种植者应用灌溉冲洗事件来去除这些有害的所谓的“压载离子”，这些离子浪费水，可能导致浆果硬度降低，风味和保质期降低，并对当地地下水质量构成风险。过量的N输入通常会导致反硝化作用导致N2 O排放量增加，和N2 O的排放量约占农业相关温室气体排放总量的44%（全球升温潜能值）。CO2的全球升温潜能值为1，而N2 O的数值为298，使后者成为更强的温室气体。通过更紧密地匹配需求与供应来减少农业和园艺中的氮投入应该有助于减少N2 O排放，但如果没有指导方针和监测传感器，这是一个有风险的策略。我们在IUK 102124中的营养需求建模工作表明，在不影响商品产量和浆果质量的情况下，基质种植树莓的氮投入可以减少32%，由于植物生物量的减少（不那么茂盛的生长），水和肥料的总需求量降低了20%。在后续项目IUK 102640中，我们开发了一种基于人工智能的氮/磷/钾（NPK）实时传感器原型，种植者可以使用该传感器来确定椰壳纤维中的NPK可用性，以告知他们的施肥决策。初步测试表明，原型系统存在一些技术问题，在进一步发展这一战略之前需要解决这些问题。将与传感器制造商合作解决传感器的局限性，并将在2022年在商业条件下评估这项工作的产出。我们还将确定一个有效的开发战略的传感器作为一个独立的手持系统，并评估集成的传感器与灌溉控制器和新技术，以提高/自动化作物养分management.This项目将展示联合收割机一个新的品种特定的N需求模型与原型传感器系统，估计NPK椰壳纤维的可用性在真实的的潜力。然后，这种方法将被集成到NetBeat（tm）平台中，并与其他技术相结合，以提供自动化控制系统。SmartNutrition系统将利用营养需求模型的输出和NPK传感器的实时反馈，在每个发育阶段将椰壳纤维NPK可用性保持在一个狭窄的最佳范围内，从而最大限度地提高可持续性。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。

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micro-nutrients will limit marketable yields and berry quality, but most guidelines on fertiliser inputs are hopelessly outdated.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.