FinerForecasts - Biologically Driven Soft-Fruit Resource Optimisation, Labour & Yield Forecasts at Plant Granularity.

FinerForecasts - 生物驱动的软果资源优化、劳动力

• 批准号: 10072185
• 金额: $ 77.9万
• 依托单位: FRUITCAST LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10072185
• 关键词: FinerForecasts; Biologically; Driven; Soft; Fruit

FinerForecasts is a collaborative project led by FruitCast, partnered with the University of Lincoln and Chambers, a soft fruit grower in Kent, UK. The project will improve the accuracy of soft fruit crop forecasting using observation-based systems. Crop forecasting is essential for fresh produce business operations as it enables farmers to match supply and demand, set forward prices, reduce waste, plan labour, and optimise resources. Existing forecast tools range from grower intuition to models powered by machine learning, and are notoriously unreliable, leading to loss of income. FinerForecasts will develop a plant mapping system, enabling plant-level forecasting, which incorporates variability between plants, improves the overall forecast accuracy, and enables growers to optimise resources and address problem spots before yield is impacted. This extends FruitCast's current observational-based forecasting approach involves taking videos of crops and locating fruit and flowers within them to predict the age, weight, and harvest date. The project aims to achieve three objectives: (i) provide reliable yield forecasts within 15% error 3 weeks ahead for entire grower sites from a biologically regulated yield forecasting model, (ii) generate plant-level, agronomically relevant maps of forecasted yield and its variability for optimisation strategies for resource allocation, and (iii) develop a digital architecture capable of scaling the developed forecasting system across multiple sites at a per-plant resolution, ready for the UK market. FinerForecasts will produce more accurate yield forecasts, contributing up to £56m(see\_outcomes\_and\_route\_to\_market) of benefits to the current strawberry market, and the generation of new tools for growers to manage crops. The project also aims to reduce waste (currently 18KT) and the CO2 emissions (46KT CO2e), water, and pesticides embedded in that waste, reducing environmental impact, driving productivity, and securing a more resilient and sustainable fresh produce sector. FinerForecasts leverages FruitCast's ability to quickly and cheaply measure crop state from videos to make plant-level forecasts possible at commercial scales, increasing forecast accuracy. The University of Lincoln's expertise in agri-robotics and AI will contribute to the project's success, and Chambers' support will enable data collection and deployment of systems. FinerForecasts will transform the soft fruit industry by providing accurate and granular yield forecasts, reducing waste and environmental impact, optimising resources, and improving the resilience and sustainability of the fresh produce sector.

FinerForeCasts是由Fruitcast领导的一个合作项目，与英国肯特的柔软水果种植者林肯大学和钱伯斯合作。该项目将使用基于观测的系统提高柔软的水果作物预测的准确性。农作物预测对于新鲜农产品业务运营至关重要，因为它使农民能够符合供求，促进价格，减少浪费，计划人工并优化资源。现有的预测工具从种植者直觉到由机器学习提供动力的模型，臭名昭著的不可靠，导致收入损失。 FinerForecasts将开发一个植物地图系统，使植物级预测能够融合植物之间的可变性，提高整体预测准确性，并使种植者能够优化资源并在产生产量之前解决问题点。这扩展了Fruitcast当前基于观察的预测方法涉及拍摄农作物的视频，并在其中找到水果和鲜花，以预测年龄，体重和收获日期。该项目旨在实现三个目标：（i）从生物学调节的产量预测模型中为整个增长站点提供可靠的产量森林，在15％的错误中，（ii）生成植物级别的，农业相关的收益率的植物级别，农业相关图像及其对资源分配的优化策略的可变性，并在数字架构中进行了定位，并在数字架构中进行稳定性，（iii II），（iii II），（iii ii II）对于英国市场。 FinerForeCasts将产生更准确的产量森林，最多贡献了5600万英镑（请参阅\ _outcomes \ _和\ _Route \ _to \ _market）对当前的草莓市场的福利，以及为种植者管理农作物的新工具而产生的新工具。该项目还旨在减少废物（目前为18KT）和二氧化碳排放（46kt CO2E），水和嵌入其中的农药，从而降低了环境影响，促进了生产力，并确保了更具抵抗力和更具可持续性的新鲜生产者行业。 FinerForecast Leverages Leverages Fruitcast能够从视频中快速，廉价地测量农作物状态，以使植物水平的森林在商业尺度上成为可能，从而提高预测准确性。林肯大学在农业机构方面的专业知识和AI的专业知识将有助于该项目的成功，而钱伯斯的支持将使数据收集和部署系统。 FinerForecasts将通过提供准确和颗粒状的产量森林，减少浪费和环境影响，优化资源以及改善新鲜生产者部门的韧性和可持续性，从而改变柔软的水果行业。