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• W3206974959 abstract "Crop monitoring becomes essential in attaining food security for implementation of various agricultural serving programs. So, fast and reliable crop monitoring is must. Using traditional methods, crop monitoring maps need high amount of satellite data downloading and processing time. Google Earth Engine (GEE) cloud platform enables us to save time in downloading and processing of time series satellite data, the every satellite imagery is converted into Normalized Difference Vegetation Index (NDVI) image and stacked monthly wise maximum images. The stacked image was used for conducting supervised classification. The main objective of this study is to evaluate the performance of different supervised machine learning (ML) classifiers in GEE platform and Spectral Matching Technique (SMT) using Sentinel-2 10 m satellite imagery in specific crop type classification. The crop classification for the year 2018–19 (rabi season) was carried for Jhansi District using supervised classifiers like Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Trees (CART) in GEE platform and also with SMT with the help of ground data. It was attained nearly 81.8% accuracy for RF, 68.8% for SVM, 64.9% for CART and 88% for SMT. The results obtained using RF classifier were nearly relative to SMT classification map. The study indicates that classifier’s performance depends on the quality of ground data used, RF can reduce the error samples in ground samples and produce satisfactory results. This study compared results obtained from all the above classifiers with agricultural statistics and also compared crop-wise accuracies. In the study, it was observed that RF classification is outperformed when compared with other classifiers considered in the study." @default.
• W3206974959 created "2021-10-25" @default.
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• W3206974959 date "2021-10-12" @default.
• W3206974959 modified "2023-09-24" @default.
• W3206974959 title "Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping" @default.
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• W3206974959 doi "https://doi.org/10.1007/978-981-16-5847-1_8" @default.
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