Satellite Image Time Series Analysis For Crop Mapping Using U-Net, Sentinel Dataset

Registration ID: IJNRD_217154

Published ID: IJNRD2404008

DOI: Click Here to Get

Malyala Chaithanya Lahari , M V Lavanya , Munnanuru Naga Dhanushya Ram , Thatha Praveen

U-Net, Convolutional Kernels, Normalization

This research project uses the U model to study time series data, from the 'Sentinel 2 – Munich' dataset with the goal of improving crop mapping accuracy. By tackling data imbalances our research enhances the precision of crop mapping, which's crucial for agricultural practices. Through data preprocessing and feature extraction techniques the U Net model shows an increase in accuracy by 90.0882%. This analysis offers insights into how crop are distributed over time and space leading to more dependable mapping results. Suggestions emphasize the need to address data imbalances for crop mapping applications providing approaches for precise and efficient crop monitoring. Ultimately this study has implications, for enhancing food security and optimizing resource allocation in agriculture.

Published Paper   E-Certificate

Malyala Chaithanya Lahari, M V Lavanya, Munnanuru Naga Dhanushya Ram, & Thatha Praveen (April-2024). Satellite Image Time Series Analysis For Crop Mapping Using U-Net, Sentinel Dataset. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), a63-a67. https://ijnrd.org/papers/IJNRD2404008.pdf

Volume 9 Issue 4, April-2024

Pages : a63-a67

Paper Reg. ID: IJNRD_217154

Published Paper Id: IJNRD2404008

Downloads: 000121985

Research Area: Computer Science & Technology 

Country: Hyderabad, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404008.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404008

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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