A0266
Title: Multi-crop land suitability prediction from remote sensing data using semi-supervised learning
Authors: Amanjot Bhullar - University of GUelph (Canada)
Khurram Nadeem - University of Guelph (Canada)
Ayesha Ali - University of Guelph (Canada) [presenting]
Abstract: Land suitability prediction involves predicting the area's crop production potential and limitations. We present a data-driven multi-layer perceptron (MLP) 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, and allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. The results of our multi-crop model may inform agricultural planning and be incorporated into cost-benefit analyses.
