Preseason crop type prediction using crop sequence boundaries

Abstract

Preseason crop-type prediction has emerged as a valuable tool for agricultural use. A reliable algorithm for early crop-type prediction has many applications, including crop mapping, planted acreage prediction, crop yield prediction, disaster response, area sample design, crop survey imputation, and more. The primary source of data for preseason crop prediction in the United States is the United States Department of Agriculture National Agricultural Statistics Service’s Cropland Data Layer, which is an annual crop specific land cover data set produced using satellite imagery and administrative data. Historical crop rotations taken from the Cropland Data Layer can be used by machine learning models to predict the future crop type in any given land area. The dataset obtained from the Cropland Data Layer is large, containing hundreds of millions of pixels per state. Current approaches for predictive modeling have utilized sampling, resulting in more scalable machine learning. In this paper, the authors propose an alternative method that uses all available Cropland Data Layer data in a rapid and memory-efficient manner. The proposed method relies on a novel technique for identifying groups of pixels with homogenous cropping history. These pixel groups are summarized as polygons representing field boundaries, referred to as crop sequence boundaries. Use of these new polygons for modeling significantly reduces the computational burden of incorporating all the Cropland Data Layer data and eliminates any increased uncertainty brought on by sampling. This novel polygon-based approach competes well with existing methods in scalability and accuracy, achieving the highest overall accuracy in 23 out of 24 tests performed.