Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation.

摘要

This study investigated the potential of developing in-season crop yield forecasting and mapping systems based on interpretation of airborne hyperspectral remote sensing imagery by machine learning algorithms. The data used for this study was obtained over a corn (Zea mays L.) field in eastern Canada.; The experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season.; Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study. (Abstract shortened by UMI.)