Premonition Net, a multi-timeline transformer network architecture towards strawberry tabletop yield forecasting

George Onoufriou*, Marc Hanheide, Georgios Leontidis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
4 Downloads (Pure)

Abstract

Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply. However yield forecasting is notoriously difficult, and oft-inaccurate. Premonition Net is a multi-timeline, time sequence ingesting approach towards processing the past, the present, and premonitions of the future. We show how this structure combined with transformers attains critical yield forecasting proficiency towards improving food security, lowering prices, and reducing waste. We find data availability to be a continued difficulty however using our premonition network and our own collected data we attain yield forecasts 3 weeks ahead with a testing set RMSE loss of 0.08 across our latest season.

Original languageEnglish
Article number107784
Number of pages8
JournalComputers and Electronics in Agriculture
Volume208
Early online date23 Mar 2023
DOIs
Publication statusPublished - 1 May 2023

Bibliographical note

This research was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship, United Kingdom [grant numbers 2155898, BB/S507453/1]

Data Availability Statement

The authors do not have permission to share data.

Keywords

  • deep learning
  • Strawberry Yield Forecasting
  • Time series
  • transformers

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