Crop Planning using Stochastic Visual Optimization

TL;DR

This paper introduces ViSeed, a visual analytics tool that predicts optimal soybean seed varieties and mixes for farmers based on weather and soil data, aiding decision-making under uncertain external factors.

Contribution

The paper presents a novel visual analytics approach for crop planning that predicts seed variety choices and optimizes planting strategies using stochastic visual optimization techniques.

Findings

Successfully predicts seed variety preferences from data

Provides visual insights to support crop planning decisions

Demonstrates effectiveness on Syngenta 2016 crop data challenge

Abstract

As the world population increases and arable land decreases, it becomes vital to improve the productivity of the agricultural land available. Given the weather and soil properties, farmers need to take critical decisions such as which seed variety to plant and in what proportion, in order to maximize productivity. These decisions are irreversible and any unusual behavior of external factors, such as weather, can have catastrophic impact on the productivity of crop. A variety which is highly desirable to a farmer might be unavailable or in short supply, therefore, it is very critical to evaluate which variety or varieties are more likely to be chosen by farmers from a growing region in order to meet demand. In this paper, we present our visual analytics tool, ViSeed, showcased on the data given in Syngenta 2016 crop data challenge 1 . This tool helps to predict optimal soybean seed variety or mix of varieties in appropriate proportions which is more likely to be chosen by farmers from a growing region. It also allows to analyse solutions generated from our approach and helps in the decision making process by providing insightful visualizations