Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units

TL;DR

This paper presents an integrated weather forecasting and optimization framework to schedule planting times in seed breeding, reducing capacity needs and ensuring consistent weekly harvests.

Contribution

It introduces a novel combination of deep neural networks and Gaussian processes for weather prediction, coupled with an optimization model for planting schedule planning.

Findings

Reduced capacity requirements by up to 69% at one site.

Achieved more consistent weekly harvest quantities.

Developed a new framework combining weather forecasting and scheduling.

Abstract

Producing higher-quality crops within shortened breeding cycles ensures global food availability and security, but this improvement intensifies logistical and productivity challenges for seed industries in the year-round breeding process due to the storage limitations. In the 2021 Syngenta crop challenge in analytics, Syngenta raised the problem to design an optimization model for the planting time scheduling in the 2020 year-round breeding process so that there is a consistent harvest quantity each week. They released a dataset that contained 2569 seed populations with their planting windows, required growing degree units for harvesting, and their harvest quantities at two sites. To address this challenge, we developed a new framework that consists of a weather time series model and an optimization model to schedule the planting time. A deep recurrent neural network was designed to predict the weather into the future, and a Gaussian process model on top of the time-series model was developed to model the uncertainty of forecasted weather. The proposed optimization models also scheduled the seed population's planting time at the fewest number of weeks with a more consistent weekly harvest quantity. Using the proposed optimization models can decrease the required capacity by 69% at site 0 and up to 51% at site 1 compared to the original planting time.