Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning.

ABSTRACT: Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013-2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop

SUBMITTER: Bhullar A 

PROVIDER: S-EPMC10133274 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature