1415圆40 - viewed 4045 times) My imagination running wild since the plot in question located on an oil refinery. We achieved the highest classification accuracy of 95% with a voting classifier ensemble.Not sure where to post this although I think I am looking at the some kind of humongous violent Crystalline result? take a gander please at the following Google earth link in 3D! ( as much as I ctrl click orbit the view and refresh I always get what looks like a crystalline city! ). 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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