Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting
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Abstract (arXiv)
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 a testing set RMSE loss of ~0.08 across our latest season.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 15 Nov 2022.
Builds on
7 of 24 references.
References · 7
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Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharingcited by 7
- 2023Tabular Machine Learning Methods for Predicting Gas Turbine Emissionscited by 1
- 2025Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learningcited by 0
- 2026Agent-Based Post-Hoc Correction of Agricultural Yield Forecastscited by 0
- 2026Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts12 May 2026
- 2025Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning25 Apr 2025
- 2023Tabular Machine Learning Methods for Predicting Gas Turbine Emissions17 Jul 2023
- 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing19 Apr 2023