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

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 aims to improve the accuracy of soft fruit crop forecasting using observation-based systems. By developing a plant mapping system that enables plant-level forecasting, FinerForecasts will incorporate variability between plants, improve overall forecast accuracy, and help growers optimise resources and address problem spots before yield is impacted. 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. The project will produce more accurate yield forecasts, contributing up to £56m of benefits to the current strawberry market, and generate new tools for growers to manage crops, reduce waste, and lower CO2 emissions, driving productivity and sustainability in the fresh produce sector.

Feedback Overview:

FinerForecasts has a strong potential to revolutionize the soft fruit industry by providing highly accurate yield forecasts and optimizing resource allocation. To further enhance its market fit, the project could consider integrating additional data sources such as weather forecasts and soil conditions. Additionally, expanding the system to other types of crops could broaden its market reach and impact.

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How can FinerForecasts ensure the scalability of its plant-level forecasting system across different types of crops?

By developing a flexible digital architecture and incorporating machine learning models that can adapt to various crop types, FinerForecasts can ensure scalability and applicability to different crops.

What are the key challenges in deploying the forecasting system at commercial scales?

Key challenges include ensuring data accuracy, handling large volumes of data, and integrating the system with existing farm management practices and technologies.

How can FinerForecasts attract investment to further develop and expand its technology?

FinerForecasts can attract investment by demonstrating the economic and environmental benefits of its technology through pilot projects and case studies, and by highlighting its potential for scalability and market impact.

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