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
<p dir="ltr">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...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| _version_ | 1864513510283673600 |
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| author | Waleed Khan (9427344) |
| author2 | Nasru Minallah (9427338) Madiha Sher (18086492) Mahmood Ali khan (18086495) Atiq ur Rehman (3044409) Tareq Al-Ansari (9872268) Amine Bermak (1895947) |
| author2_role | author author author author author author |
| author_facet | Waleed Khan (9427344) Nasru Minallah (9427338) Madiha Sher (18086492) Mahmood Ali khan (18086495) Atiq ur Rehman (3044409) Tareq Al-Ansari (9872268) Amine Bermak (1895947) |
| author_role | author |
| dc.creator.none.fl_str_mv | Waleed Khan (9427344) Nasru Minallah (9427338) Madiha Sher (18086492) Mahmood Ali khan (18086495) Atiq ur Rehman (3044409) Tareq Al-Ansari (9872268) Amine Bermak (1895947) |
| dc.date.none.fl_str_mv | 2024-03-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0299350 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/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/26363131 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Engineering Geomatic engineering Information and computing sciences Machine learning Agricultural Remote Sensing Crop Mapping Bidirectional Long Short-Term Memory (Bi-LSTM) Crop Classification Open Source Tools Smallholder Economies Deep Learning |
| dc.title.none.fl_str_mv | 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 |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0299350" target="_blank">https://dx.doi.org/10.1371/journal.pone.0299350</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ef97d69a00d309382dc4515edd3d5a1b |
| identifier_str_mv | 10.1371/journal.pone.0299350 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26363131 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classificationWaleed Khan (9427344)Nasru Minallah (9427338)Madiha Sher (18086492)Mahmood Ali khan (18086495)Atiq ur Rehman (3044409)Tareq Al-Ansari (9872268)Amine Bermak (1895947)Agricultural, veterinary and food sciencesAgriculture, land and farm managementCrop and pasture productionEngineeringGeomatic engineeringInformation and computing sciencesMachine learningAgricultural Remote SensingCrop MappingBidirectional Long Short-Term Memory (Bi-LSTM)Crop ClassificationOpen Source ToolsSmallholder EconomiesDeep Learning<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0299350" target="_blank">https://dx.doi.org/10.1371/journal.pone.0299350</a></p>2024-03-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0299350https://figshare.com/articles/journal_contribution/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/26363131CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263631312024-03-01T00:00:00Z |
| spellingShingle | 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 Waleed Khan (9427344) Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Engineering Geomatic engineering Information and computing sciences Machine learning Agricultural Remote Sensing Crop Mapping Bidirectional Long Short-Term Memory (Bi-LSTM) Crop Classification Open Source Tools Smallholder Economies Deep Learning |
| status_str | publishedVersion |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Engineering Geomatic engineering Information and computing sciences Machine learning Agricultural Remote Sensing Crop Mapping Bidirectional Long Short-Term Memory (Bi-LSTM) Crop Classification Open Source Tools Smallholder Economies Deep Learning |