Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units
Javad Ansarifar, Faezeh Akhavizadegan, and Lizhi Wang

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…
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Taxonomy
TopicsSmart Agriculture and AI · Greenhouse Technology and Climate Control · Evolutionary Algorithms and Applications
MethodsGaussian Process
