Seed Stocking Via Multi-Task Learning

by   Yunhe Feng, et al.

Sellers of crop seeds need to plan for the variety and quantity of seeds to stock at least a year in advance. There are a large number of seed varieties of one crop, and each can perform best under different growing conditions. Given the unpredictability of weather, farmers need to make decisions that balance high yield and low risk. A seed vendor needs to be able to anticipate the needs of farmers and have them ready. In this study, we propose an analytical framework for estimating seed demand with three major steps. First, we will estimate the yield and risk of each variety as if they were planted at each location. Since past experiments performed with different seed varieties are highly unbalanced across varieties, and the combination of growing conditions is sparse, we employ multi-task learning to borrow information from similar varieties. Second, we will determine the best mix of seeds for each location by seeking a tradeoff between yield and risk. Third, we will aggregate such mix and pick the top five varieties to re-balance the yield and risk for each growing location. We find that multi-task learning provides a viable solution for yield prediction, and our overall analytical framework has resulted in a good performance.


Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans

Eradicating hunger and malnutrition is a key development goal of the 21s...

Multi-Task Learning for Mental Health using Social Media Text

We introduce initial groundwork for estimating suicide risk and mental h...

Distributed Stochastic Multi-Task Learning with Graph Regularization

We propose methods for distributed graph-based multi-task learning that ...

Crop Planning using Stochastic Visual Optimization

As the world population increases and arable land decreases, it becomes ...

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

Producing higher-quality crops within shortened breeding cycles ensures ...

Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring

Inferring the source information of greenhouse gases, such as methane, f...

Please sign up or login with your details

Forgot password? Click here to reset