Standard & Poor’s and S&P are registered trademarks of Standard & Poor’s Financial Services LLC and Dow Jones is a registered trademark of Dow Jones Trademark Holdings LLC. Dow Jones: The Dow Jones branded indices are proprietary to and are calculated, distributed and marketed by DJI Opco, a subsidiary of S&P Dow Jones Indices LLC and have been licensed for use to S&P Opco, LLC and CNN. Chicago Mercantile: Certain market data is the property of Chicago Mercantile Exchange Inc. US market indices are shown in real time, except for the S&P 500 which is refreshed every two minutes. We achieved the highest classification accuracy of 95% with a voting classifier ensemble.Your CNN account Log in to your CNN account In this paper, we implemented traditional machine learning models like Random Forest, Decision Tree and SVM. Cropland Data Layer (CDL), which is the product of National Statistics Service (NASS), has been used for classifying crop types and ground survey data for Fresno County has been used for testing the model. Landsat 7 satellite image data from Google Earth Engine has been used to calculate various vegetation indexes such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) and leaf area index (LAI). For our research, we chose Fresno County in California as the area of interest, as it has more than a hundred types of crops grown each year as well as an abundance of ground available for surveying and collecting data.
The availability of remote sensing images through open-source platforms, such as Google Earth Engine, has enabled researchers to work on data-driven agriculture mapping.
Agriculture plays a vital role in the United States economy with California being the foremost state in agriculture production. The potential of machine learning models in satellite remote sensing data has earned a significant amount of attention. Due to the significant growth in sensor technologies, a great deal of data has been made available in the public domain. With the advancement of machine learning algorithms, different models are being used in agriculture monitoring and food industries.
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