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Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification

journal contribution
submitted on 2024-07-25, 07:03 and posted on 2024-07-25, 07:41 authored by Waleed Khan, Nasru Minallah, Madiha Sher, Mahmood Ali khan, Atiq ur Rehman, Tareq Al-Ansari, Amine Bermak

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

Other Information

Published in: PLOS ONE
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1371/journal.pone.0299350

Funding

Qatar National Research Fund (MME01-0922-190049), Developing National Food Security Intelligence.

History

Language

  • English

Publisher

Public Library of Science (PLoS)

Publication Year

  • 2024

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

Related Datasets

Khan Waleed. (2024). Training-Data-CSVs-for-PLOSOne. Last modified 2024. GitHub Repository. https://github.com/khanwaleed1011/Training-Data-CSVs-for-PLOSOne