Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images
Paddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. Ho...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Egyptian Journal of Remote Sensing and Space Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110982323000935 |
| _version_ | 1797372038822756352 |
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| author | Samera Samsuddin Sah Khairul Nizam Abdul Maulud Suraya Sharil Othman A. Karim Biswajeet Pradhan |
| author_facet | Samera Samsuddin Sah Khairul Nizam Abdul Maulud Suraya Sharil Othman A. Karim Biswajeet Pradhan |
| author_sort | Samera Samsuddin Sah |
| collection | DOAJ |
| description | Paddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. However, the prominent paddy-producing state of Kedah has witnessed a decline in yields over the years. To address this, the study explores the effectiveness of unmanned aerial vehicles (UAVs) equipped with vegetation indices (VIs) for monitoring paddy plant health at various growth stages. Researchers acquired aerial imagery during two seasons in 2019, capturing three distinct growth stages: tillering (40 days after sowing), flowering (60 days after sowing), and ripening (100 days after sowing). These stages represent critical points in the paddy plant's life cycle. Agisoft Metashape software processed the images to extract VIs data. The study found that the Normalized Difference Vegetation Index (NDVI) and Blue Normalized Difference Vegetation Index (BNDVI) exhibited over 90% similarity. In contrast, the Normalized Difference Red Edge Index (NDRE), utilizing near-infrared and red-edge light reflections, demonstrated a unique relationship. NDRE outperformed NDVI and BNDVI with an R-squared value of 0.842, showcasing its superior accuracy, especially for dense crops like paddy plants sensitive to subtle changes in vegetation. In conclusion, this research highlights the potential of UAV-based VIs for effectively monitoring paddy plant health during different growth stages. The NDRE index, in particular, proves valuable for assessing dense crops, offering insights for precision agriculture and crop management in Malaysia. |
| first_indexed | 2024-03-08T18:30:41Z |
| format | Article |
| id | doaj.art-5bc4ed9ba8e646638d80dc985aa9d056 |
| institution | Directory Open Access Journal |
| issn | 1110-9823 |
| language | English |
| last_indexed | 2024-03-08T18:30:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Journal of Remote Sensing and Space Sciences |
| spelling | doaj.art-5bc4ed9ba8e646638d80dc985aa9d0562023-12-30T04:41:11ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232023-12-01264989998Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV imagesSamera Samsuddin Sah0Khairul Nizam Abdul Maulud1Suraya Sharil2Othman A. Karim3Biswajeet Pradhan4Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia; Water Research and Environmental Sustainability Growth (WAREG), Center of Excellent (COE), Universiti Malaysia Perlis, 02600 Arau, Perlis, MalaysiaDepartment of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Corresponding author at: Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MalaysiaDepartment of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MalaysiaEarth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, 2007 NSW, AustraliaPaddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. However, the prominent paddy-producing state of Kedah has witnessed a decline in yields over the years. To address this, the study explores the effectiveness of unmanned aerial vehicles (UAVs) equipped with vegetation indices (VIs) for monitoring paddy plant health at various growth stages. Researchers acquired aerial imagery during two seasons in 2019, capturing three distinct growth stages: tillering (40 days after sowing), flowering (60 days after sowing), and ripening (100 days after sowing). These stages represent critical points in the paddy plant's life cycle. Agisoft Metashape software processed the images to extract VIs data. The study found that the Normalized Difference Vegetation Index (NDVI) and Blue Normalized Difference Vegetation Index (BNDVI) exhibited over 90% similarity. In contrast, the Normalized Difference Red Edge Index (NDRE), utilizing near-infrared and red-edge light reflections, demonstrated a unique relationship. NDRE outperformed NDVI and BNDVI with an R-squared value of 0.842, showcasing its superior accuracy, especially for dense crops like paddy plants sensitive to subtle changes in vegetation. In conclusion, this research highlights the potential of UAV-based VIs for effectively monitoring paddy plant health during different growth stages. The NDRE index, in particular, proves valuable for assessing dense crops, offering insights for precision agriculture and crop management in Malaysia.http://www.sciencedirect.com/science/article/pii/S1110982323000935Rice growth phase monitoringUAVMultispectralGeospatial |
| spellingShingle | Samera Samsuddin Sah Khairul Nizam Abdul Maulud Suraya Sharil Othman A. Karim Biswajeet Pradhan Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images Egyptian Journal of Remote Sensing and Space Sciences Rice growth phase monitoring UAV Multispectral Geospatial |
| title | Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images |
| title_full | Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images |
| title_fullStr | Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images |
| title_full_unstemmed | Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images |
| title_short | Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images |
| title_sort | monitoring of three stages of paddy growth using multispectral vegetation index derived from uav images |
| topic | Rice growth phase monitoring UAV Multispectral Geospatial |
| url | http://www.sciencedirect.com/science/article/pii/S1110982323000935 |
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